Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey
Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accur...
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| Published in | Computer communications Vol. 170; pp. 19 - 41 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
15.03.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0140-3664 1873-703X |
| DOI | 10.1016/j.comcom.2021.01.021 |
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| Abstract | Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications. |
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| AbstractList | Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications. |
| Author | Taherkordi, Amir Abbasi, Mahmoud Shahraki, Amin |
| Author_xml | – sequence: 1 givenname: Mahmoud orcidid: 0000-0002-1886-8284 surname: Abbasi fullname: Abbasi, Mahmoud organization: Department of Computer Sciences, Islamic Azad University, Mashhad, Iran – sequence: 2 givenname: Amin orcidid: 0000-0002-5065-9968 surname: Shahraki fullname: Shahraki, Amin email: am.shahraki@ieee.org organization: Department of Informatics, University of Oslo, Oslo, Norway – sequence: 3 givenname: Amir surname: Taherkordi fullname: Taherkordi, Amir organization: Department of Informatics, University of Oslo, Oslo, Norway |
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| Cites_doi | 10.3390/info10040122 10.1126/science.1127647 10.1162/089976602760128018 10.1109/TSUSC.2018.2809665 10.14778/2367502.2367572 10.1109/TNSM.2019.2927886 10.1016/j.amc.2014.06.108 10.1109/TIFS.2017.2730819 10.1162/neco.1997.9.8.1735 10.1145/3155133.3155175 10.1038/nature14539 10.1109/TNSM.2020.3035315 10.1145/2940116.2940117 10.1109/ACCESS.2015.2467174 10.1109/TSC.2016.2599878 10.1109/TMC.2018.2870135 10.1109/SURV.2011.092311.00031 10.1109/SURV.2013.052213.00000 10.1109/MNET.2019.1800458 10.1016/j.future.2012.09.001 10.1109/COMST.2018.2825786 10.1145/1559845.1559865 10.1145/3021304.3021305 10.1109/TNSM.2019.2933358 10.1109/MNET.2014.6963801 10.1155/2018/1260860 10.1016/j.comnet.2014.03.007 10.1109/MNET.2014.6863129 10.1109/MPRV.2018.03367731 10.1109/MNET.2018.1800078 10.1109/ACCESS.2014.2325029 10.1109/COMST.2018.2844341 10.1109/ACCESS.2016.2613278 10.1109/ACCESS.2019.2893871 10.1186/s13174-018-0087-2 10.1109/COMST.2017.2694469 10.1016/j.scs.2017.12.022 10.1109/MWC.2006.275197 10.1109/ACCESS.2019.2938410 10.1016/j.eswa.2019.04.064 10.1007/s00500-019-04030-2 10.1109/TITS.2020.3027390 10.1145/3298981 10.1145/2076796.2082137 10.1016/j.cose.2019.101645 10.1109/SURV.2012.021312.00116 10.1109/COMST.2017.2707140 10.1109/TLA.2016.7430108 10.1186/s13174-015-0041-5 10.1145/2043164.2018452 10.1109/MNET.2016.7474340 10.3390/su11010189 10.1145/3126555 10.1109/ACCESS.2018.2863036 10.1109/JSAC.2019.2904348 10.1145/2785956.2787502 10.1109/MNET.2012.6135854 10.1109/SURV.2012.100412.00017 10.1145/2413176.2413205 10.1093/ietcom/e88-b.10.3992 10.1016/j.giq.2018.10.001 10.1109/COMST.2017.2719862 10.1145/3209582.3209606 10.1145/3167132.3167306 10.1016/j.compeleceng.2020.106955 10.1109/ACCESS.2018.2872430 10.1109/MCOM.2019.1800819 10.1016/j.adhoc.2020.102258 10.1109/ACCESS.2017.2747560 10.1109/MCOM.001.1900283 10.1109/ACCESS.2017.2787696 10.1504/IJBDI.2016.073903 10.1109/JIOT.2018.2832071 10.1109/LCOMM.2018.2841832 10.1016/j.comnet.2020.107376 10.1007/s11227-016-1805-9 10.1109/MNET.2018.1700411 10.1016/j.jnca.2018.05.004 10.1007/s00521-018-3595-x 10.1016/j.jbusres.2016.08.001 10.1145/3314545.3316278 10.1109/ACCESS.2017.2780250 10.1109/MCOM.2014.6829969 10.1109/JIOT.2019.2908019 10.1016/j.cose.2018.11.001 10.1016/j.engappai.2020.103770 10.1109/MCOM.2018.1700332 10.1109/ACCESS.2019.2962106 10.1145/1390156.1390290 10.1109/TNSM.2019.2899085 10.1186/s40537-014-0007-7 10.1109/MNET.2018.1800127 10.1109/TCAD.2018.2858384 10.1145/3291533.3291540 10.1109/TKDE.2019.2946162 10.1186/s40537-019-0206-3 10.1109/COMST.2016.2570599 |
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| Keywords | Deep learning Survey Network Traffic Monitoring and Analysis QoS Machine learning Edge Intelligence Network management NTMA IoT |
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| References | Labrinidis, Jagadish (b10) 2012; 5 Rezaei, Liu (b27) 2019; 57 Miller, Curran, Lunney (b90) 2018 Sutskever, Vinyals, Le (b66) 2014 Cecil (b3) 2006 Aliu, Imran, Imran, Evans (b157) 2012; 15 Ren, He, Girshick, Sun (b69) 2015 Bega, Gramaglia, Fiore, Banchs, Costa-Perez (b131) 2019 Goodfellow, Bengio, Courville (b56) 2016 Ehrlich, Biendarra, Trsek, Wojtkowiak, Jasperneite (b8) 2017 Chen, Lin (b47) 2014; 2 Mulvey, Foh, Imran, Tafazolli (b139) 2019; 7 Konečnỳ, McMahan, Yu, Richtárik, Suresh, Bacon (b79) 2016 Qi, Yao, Uzunov (b149) 2017 Tikunov, Nishimura (b117) 2007 Yousefi-Azar, Varadharajan, Hamey, Tupakula (b167) 2017 Motamedi, Fong, Ghiasi (b185) 2017; 16 Graves (b67) 2013 Yeo, Koo, Yoon, Hwang, Ryu, Song, Park (b97) 2018 Imran, Zoha, Abu-Dayya (b109) 2014; 28 Masood, Asghar, Imran, Mian (b146) 2018 Shahraki, Taherkordi, Haugen, Eliassen (b14) 2020 Meidan, Bohadana, Mathov, Mirsky, Shabtai, Breitenbacher, Elovici (b178) 2018; 17 ElSawy, Hossain, Haenggi (b156) 2013; 15 Verma, Kawamoto, Fadlullah, Nishiyama, Kato (b31) 2017; 19 Zhao, Zhang, Sang (b102) 2019 Najafabadi, Villanustre, Khoshgoftaar, Seliya, Wald, Muharemagic (b25) 2015; 2 Hardy, Chen, Hou, Ye, Li (b174) 2016 T. Benson, A. Akella, D. Maltz, Unraveling the complexity of network management, in: Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, ser. NSDI’09, 2009. Abadi, Agarwal, Barham, Brevdo, Chen, Citro, Corrado, Davis, Dean, Devin, Ghemawat, Goodfellow, Harp, Irving, Isard, Jia, Jozefowicz, Kaiser, Kudlur, Levenberg, Mane, Monga, Moore, Murray, Olah, Schuster, Shlens, Steiner, Sutskever, Talwar, Tucker, Vanhoucke, Vasudevan, Viegas, Vinyals, Warden, Wattenberg, Wicke, Yu, Zheng (b181) 2016 Ricciato (b32) 2006; 13 Nie, Wang, Wan, Yu, Song, Jiang (b135) 2018; 2018 Mahmoud Abbasi, Amin Shahraki, Hamid R Barzegar, Claus Pahl, Synchronization Techniques in “Device to Device-and Vehicle to Vehicle-Enabled” Cellular Networks: A survey, Comput. Electr. Eng., 90 106955. Wang, Zhu, Zeng, Ye, Sheng (b100) 2017 (b40) 2020 Abduljabbar, Dia, Liyanage, Bagloee (b52) 2019; 11 (b39) 2020 Marinchak, Forrest, Hoanca (b54) 2018 So-In (b2) 2009 Alsheikh, Niyato, Lin, Tan, Han (b76) 2016; 30 Roh, Heo, Whang (b180) 2019 Hinton, Salakhutdinov (b72) 2006; 313 Suter (b57) 1990; 1 Masala, Servetti, Basso, De Martin (b15) 2014 Naseer, Saleem, Khalid, Bashir, Han, Iqbal, Han (b163) 2018; 6 Ferreira, Shinoda (b84) 2016; 14 Asghar, Farooq, Imran (b153) 2018; 20 Rathore, Paul, Hong, Seo, Awan, Saeed (b24) 2018; 40 Pedro Casas, Alessandro D Alconzo, Tanja Zseby, Marco Mellia, Big-DAMA: big data analytics for network traffic monitoring and analysis, in: Proceedings of the 2016 Workshop on Fostering Latin-American Research in Data Communication Networks, 2016, pp. 1–3. Li, Chen, Yang, Gao, Zhang, Deen (b103) 2018; 32 Lo’ai, Mehmood, Benkhlifa, Song (b46) 2016; 4 Kiros, Salakhutdinov, Zemel (b68) 2014 Azmoodeh, Dehghantanha, Choo (b175) 2018; 4 D Alconzo, Drago, Morichetta, Mellia, Casas (b1) 2019; 16 Oliner, Ganapathi, Xu (b21) 2011; 9 Chaoyun Zhang, Paul Patras, Long-term mobile traffic forecasting using deep spatio-temporal neural networks, in: Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2018, pp. 231–240. Gupta, Salman, Das, Erbad, Jain, Samaka (b148) 2019 Liu, Li, Wu, Li (b50) 2018; 32 Gheisari, Wang, Bhuiyan (b26) 2017 Qing Lyu, Xingjian Lu, Effective Media Traffic Classification Using Deep Learning, in: Proceedings of the 2019 3rd International Conference on Compute and Data Analysis, 2019, pp. 139–146. Shahraki, Haugen (b13) 2019 Haytham Assem, Declan O’Sullivan, Discovering new socio-demographic regional patterns in cities, in: Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, 2016, pp. 1–9. Hinton (b74) 2002; 14 Ly Vu, Cong Thanh Bui, Quang Uy Nguyen, A deep learning based method for handling imbalanced problem in network traffic classification, in: Proceedings of the Eighth International Symposium on Information and Communication Technology, 2017, pp. 333–339. Joshi, Hadi (b121) 2015 Dusia, Sethi (b147) 2016; 18 Boutaba, Salahuddin, Limam, Ayoubi, Shahriar, Estrada-Solano, Caicedo (b9) 2018; 9 Shu, Zhou, Zhang, Du, Guizani (b171) 2020 Pektaş, Acarman (b177) 2019; 31 Aceto, Ciuonzo, Montieri, Pescapé (b81) 2019; 16 Huang, Zhao, Huang, Guo (b144) 2020; 58 Van Efferen, Ali-Eldin (b89) 2017 Bär, Finamore, Casas, Golab, Mellia (b37) 2014 Shahraki, Geitle, Haugen (b12) 2020; 31 Mohammadi, Al-Fuqaha, Sorour, Guizani (b77) 2018; 20 Modi, Acha (b160) 2017; 73 Barabas, Boanea, Rus, Dobrota, Domingo-Pascual (b122) 2011 Salih, Abdulrazaq (b87) 2019 Hu, Guo, Ratazzi, Gong (b189) 2020 Feng, Chen, Gao, Zeng, Li (b126) 2018; 32 Hwang, Chen (b45) 2017 Demchenko, De Laat, Membrey (b22) 2014 Iliyasu, Deng (b107) 2019; 8 Anestis Dalgkitsis, Malamati Louta, George T. Karetsos, Traffic forecasting in cellular networks using the LSTM RNN, in: Proceedings of the 22nd Pan-Hellenic Conference on Informatics, 2018, pp. 28–33. Ding, Ding, Morozov, Fabarisov, Janschek (b151) 2019 Zhang, Xu, Min, Jiang, Pelechrinis, Zhang (b154) 2016 Jebara (b75) 2012 Wang, Zhu, Wang, Zeng, Yang (b93) 2017 Zhong, Gu (b173) 2019; 133 Tong, Tran, Souihi, Mellouk (b99) 2018 Thing (b168) 2017 Sundaresan, De Donato, Feamster, Teixeira, Crawford, Pescapè (b17) 2011; 41 Cooley, Mobasher, Srivastava (b38) 1997 Graves, Jaitly (b64) 2014 Li, Zhao, Zhou, Palicot, Zhang (b108) 2014; 52 Oliveira, Barbar, Soares (b130) 2014 Jiang, Wu, Wang, Xin (b162) 2018 Sivarajah, Kamal, Irani, Weerakkody (b11) 2017; 70 Torres, Catania, Garcia, Garino (b179) 2016 Wang, Tao, Ranjan, Marten, Streit, Chen, Chen (b41) 2013; 29 Basso, Servetti, De Martin (b16) 2010 da Rocha Fonseca, Mota (b141) 2017; 19 Al Nuaimi, Al Neyadi, Mohamed, Al-Jaroodi (b44) 2015; 6 Huang, Chiang, Li (b113) 2017 Mismar, Evans (b152) 2018 Oh, Wang, Tang, Sjoding, Wiens (b61) 2019 Alom, Bontupalli, Taha (b106) 2015 LeCun (b58) 1989 Xu, Lin, Huang, Wu, Shi, Song, Li (b110) 2016; 9 Bega, Gramaglia, Fiore, Banchs, Costa-Perez (b132) 2020 Khunteta, Chavva (b150) 2017 Dainotti, Pescape, Claffy (b80) 2012; 26 Tijmen Tieleman, Training restricted Boltzmann machines using approximations to the likelihood gradient, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 1064–1071. Cisco (b111) 2012 ITU, global ICT statistics. Berman, Buczak, Chavis, Corbett (b155) 2019; 10 Hochreiter, Schmidhuber (b63) 1997; 9 Zhou, Yan, Fu, Yao (b18) 2018; 116 Papamartzivanos, Mármol, Kambourakis (b161) 2019; 7 Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel J Abadi, David J DeWitt, Samuel Madden, Michael Stonebraker, A comparison of approaches to large-scale data analysis, in: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, 2009, pp. 165–178. Wang, Tuor, Salonidis, Leung, Makaya, He, Chan (b188) 2019; 37 Salakhutdinov, Hinton (b73) 2009 Cao, Zhong, Zhou, Wang, Zhu, Zhang (b134) 2017; 6 Chen, Zhang, Maharjan, Alam, Wu (b169) 2019; 33 Bkassiny, Li, Jayaweera (b184) 2012; 15 Hariri, Fredericks, Bowers (b23) 2019; 6 Morariu, Stiller (b19) 2008 Lotfollahi, Siavoshani, Zade, Saberian (b49) 2020; 24 Hongyi Zeng, Peyman Kazemian, George Varghese, Nick McKeown, Automatic test packet generation, in: Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies, 2012, pp. 241–252. Fang, Cheng, Wang, Yang (b128) 2018; 5 Wang, Tang, Xu, Wang, Xue, Zhang, Yang (b42) 2017 Sreekesh (b88) 2016 Garg, Kaur, Kumar, Kaddoum, Zomaya, Ranjan (b166) 2019; 16 Shahraki, Abbasi, Haugen (b35) 2020; 94 Fadlullah, Tang, Mao, Kato, Akashi, Inoue, Mizutani (b30) 2017; 19 Wang, Ye, Chen, Qian (b82) 2018; 6 Lopez-Martin, Carro, Sanchez-Esguevillas, Lloret (b98) 2017; 5 Conti, Li, Maragno, Spolaor (b29) 2017 Liu, Liu, Ansari (b6) 2014; 28 Tavallaee, Bagheri, Lu, Ghorbani (b164) 2009 Han, Kim, Ju, Hong (b7) 2002 Sahay, Geethakumari, Modugu, Mitra (b91) 2018 Madan, SarathiMangipudi (b124) 2018 Mulvey, Foh, Imran, Tafazolli (b145) 2018 Androutsopoulou, Karacapilidis, Loukis, Charalabidis (b53) 2019; 36 Yang, Liu, Chen, Tong (b78) 2019; 10 Chen, He, Li, Geng (b94) 2017 Lee, Levanti, Kim (b20) 2014; 65 Wang, Lu, Qin (b92) 2020; 88 Ucci, Aniello, Baldoni (b28) 2019; 81 Wang, Li, Leung (b183) 2015; 3 Shu, Yu, Yang, Liu, Feng (b118) 2005; 88 Malaiya, Kwon, Kim, Suh, Kim, Kim (b165) 2018 Dubin, Dvir, Pele, Hadar (b33) 2017; 12 Nguyen, Hoang, Niyato, Wang, Nguyen, Dutkiewicz (b170) 2018 Salek, Madani, Azmi (b86) 2013 Assem, Caglayan, Buda, O’Sullivan (b114) 2018 Andreoletti, Troia, Musumeci, Giordano, Maier, Tornatore (b125) 2019 Zhang, Zhang, Yuan, Zhang (b133) 2018; 22 Shahraki, Taherkordi, Haugen, Eliassen (b5) 2020; 180 Fahlman, Hinton, Sejnowski (b70) 1983 Terran Lane, Carla E. Brodley, An application of machine learning to anomaly detection, in: Proceedings of the 20th National Information Systems Security Conference, Vol. 377, Baltimore, USA, 1997, pp. 366–380. Rafsanjani, Rezaei, Shahraki, Saeid (b119) 2014; 244 Shahraki, Taherzadeh, Haugen (b4) 2017 Radford, Apolonio, Trias, Simpson (b101) 2018 Wang, Zhou, Xiao, Xing, Yang, Liu, Peng (b137) 2018; 18 Oliveira, Barbar, Soares (b120) 2016; 3 Jos van Roosmalen, Harald Vranken, Marko van Eekelen, Applying deep learning on packet flows for botnet detection, in: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018, pp. 1629–1636. LeCun, Bengio, Hinton (b51) 2015; 521 Pwint, Shwe (b85) 2019 Graves, Schmidhuber (b65) 2009 Abeshu, Chilamkurti (b104) 2018; 56 Chollet (b182) 2015 Li, Wang, Wang, Zheng (b136) 2020; 107 Justine Sherry, Chang Lan, Raluca Ada Popa, Sylvia Ratnasamy, Blindbox: Deep packet inspection over encrypted traffic, in: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, 2015, pp. 213–226. Zhao, Barijough, Gerstlauer (b187) 2018; 37 Rezaei, Liu (b95) 2018 Gruslys, Munos, Danihelka, Lanctot, Graves (b62) 2016 Hasan, Boostanimehr, Bhargava (b112) 2011; 13 Srinivasan, Truong-Huu, Gurusamy (b143) 2019; 6 Salmi, Kiljander, Pakkala (b59) 2020 Rumelhart, Hinton, Williams (b60) 1985 Az Ehrlich (10.1016/j.comcom.2021.01.021_b8) 2017 Hinton (10.1016/j.comcom.2021.01.021_b72) 2006; 313 Imteaj (10.1016/j.comcom.2021.01.021_b186) 2020 Meidan (10.1016/j.comcom.2021.01.021_b178) 2018; 17 Morariu (10.1016/j.comcom.2021.01.021_b19) 2008 Oliveira (10.1016/j.comcom.2021.01.021_b130) 2014 Azmoodeh (10.1016/j.comcom.2021.01.021_b175) 2018; 4 Yousefi-Azar (10.1016/j.comcom.2021.01.021_b167) 2017 Alom (10.1016/j.comcom.2021.01.021_b106) 2015 Tikunov (10.1016/j.comcom.2021.01.021_b117) 2007 Qi (10.1016/j.comcom.2021.01.021_b149) 2017 Chen (10.1016/j.comcom.2021.01.021_b169) 2019; 33 Papamartzivanos (10.1016/j.comcom.2021.01.021_b161) 2019; 7 Aliu (10.1016/j.comcom.2021.01.021_b157) 2012; 15 Dainotti (10.1016/j.comcom.2021.01.021_b80) 2012; 26 Gruslys (10.1016/j.comcom.2021.01.021_b62) 2016 Salek (10.1016/j.comcom.2021.01.021_b86) 2013 Huang (10.1016/j.comcom.2021.01.021_b113) 2017 Wang (10.1016/j.comcom.2021.01.021_b42) 2017 10.1016/j.comcom.2021.01.021_b127 Berman (10.1016/j.comcom.2021.01.021_b155) 2019; 10 10.1016/j.comcom.2021.01.021_b123 Shahraki (10.1016/j.comcom.2021.01.021_b5) 2020; 180 Shahraki (10.1016/j.comcom.2021.01.021_b14) 2020 10.1016/j.comcom.2021.01.021_b129 Lopez-Martin (10.1016/j.comcom.2021.01.021_b98) 2017; 5 Lee (10.1016/j.comcom.2021.01.021_b20) 2014; 65 Lotfollahi (10.1016/j.comcom.2021.01.021_b49) 2020; 24 Cisco (10.1016/j.comcom.2021.01.021_b111) 2012 Dusia (10.1016/j.comcom.2021.01.021_b147) 2016; 18 Najafabadi (10.1016/j.comcom.2021.01.021_b25) 2015; 2 Shahraki (10.1016/j.comcom.2021.01.021_b35) 2020; 94 10.1016/j.comcom.2021.01.021_b71 Pektaş (10.1016/j.comcom.2021.01.021_b177) 2019; 31 Zhang (10.1016/j.comcom.2021.01.021_b133) 2018; 22 Miller (10.1016/j.comcom.2021.01.021_b90) 2018 Bega (10.1016/j.comcom.2021.01.021_b132) 2020 Baras (10.1016/j.comcom.2021.01.021_b142) 1997 10.1016/j.comcom.2021.01.021_b172 Dubin (10.1016/j.comcom.2021.01.021_b33) 2017; 12 Jiang (10.1016/j.comcom.2021.01.021_b162) 2018 Kiros (10.1016/j.comcom.2021.01.021_b68) 2014 Androutsopoulou (10.1016/j.comcom.2021.01.021_b53) 2019; 36 Tavallaee (10.1016/j.comcom.2021.01.021_b164) 2009 Radford (10.1016/j.comcom.2021.01.021_b101) 2018 10.1016/j.comcom.2021.01.021_b176 Sundaresan (10.1016/j.comcom.2021.01.021_b17) 2011; 41 Zhao (10.1016/j.comcom.2021.01.021_b102) 2019 da Rocha Fonseca (10.1016/j.comcom.2021.01.021_b141) 2017; 19 Li (10.1016/j.comcom.2021.01.021_b103) 2018; 32 Bega (10.1016/j.comcom.2021.01.021_b131) 2019 Joshi (10.1016/j.comcom.2021.01.021_b121) 2015 Bär (10.1016/j.comcom.2021.01.021_b37) 2014 (10.1016/j.comcom.2021.01.021_b40) 2020 Al Nuaimi (10.1016/j.comcom.2021.01.021_b44) 2015; 6 Gupta (10.1016/j.comcom.2021.01.021_b148) 2019 Conti (10.1016/j.comcom.2021.01.021_b29) 2017 Mohammadi (10.1016/j.comcom.2021.01.021_b77) 2018; 20 Cooley (10.1016/j.comcom.2021.01.021_b38) 1997 Goodfellow (10.1016/j.comcom.2021.01.021_b56) 2016 Shahraki (10.1016/j.comcom.2021.01.021_b4) 2017 Hinton (10.1016/j.comcom.2021.01.021_b74) 2002; 14 10.1016/j.comcom.2021.01.021_b83 Rezaei (10.1016/j.comcom.2021.01.021_b95) 2018 Sivarajah (10.1016/j.comcom.2021.01.021_b11) 2017; 70 Thing (10.1016/j.comcom.2021.01.021_b168) 2017 Srinivasan (10.1016/j.comcom.2021.01.021_b143) 2019; 6 Wang (10.1016/j.comcom.2021.01.021_b92) 2020; 88 Ricciato (10.1016/j.comcom.2021.01.021_b32) 2006; 13 D Alconzo (10.1016/j.comcom.2021.01.021_b1) 2019; 16 Modi (10.1016/j.comcom.2021.01.021_b160) 2017; 73 10.1016/j.comcom.2021.01.021_b105 Hasan (10.1016/j.comcom.2021.01.021_b112) 2011; 13 Cao (10.1016/j.comcom.2021.01.021_b134) 2017; 6 Roh (10.1016/j.comcom.2021.01.021_b180) 2019 Wang (10.1016/j.comcom.2021.01.021_b82) 2018; 6 Suter (10.1016/j.comcom.2021.01.021_b57) 1990; 1 Shahraki (10.1016/j.comcom.2021.01.021_b13) 2019 Shahraki (10.1016/j.comcom.2021.01.021_b12) 2020; 31 Salih (10.1016/j.comcom.2021.01.021_b87) 2019 Ding (10.1016/j.comcom.2021.01.021_b151) 2019 Hardy (10.1016/j.comcom.2021.01.021_b174) 2016 Fadlullah (10.1016/j.comcom.2021.01.021_b30) 2017; 19 Mulvey (10.1016/j.comcom.2021.01.021_b139) 2019; 7 Verma (10.1016/j.comcom.2021.01.021_b31) 2017; 19 (10.1016/j.comcom.2021.01.021_b159) 2019 LeCun (10.1016/j.comcom.2021.01.021_b58) 1989 Imran (10.1016/j.comcom.2021.01.021_b109) 2014; 28 Marinchak (10.1016/j.comcom.2021.01.021_b54) 2018 ElSawy (10.1016/j.comcom.2021.01.021_b156) 2013; 15 Shu (10.1016/j.comcom.2021.01.021_b171) 2020 Demchenko (10.1016/j.comcom.2021.01.021_b22) 2014 Russell (10.1016/j.comcom.2021.01.021_b55) 2002 Mismar (10.1016/j.comcom.2021.01.021_b152) 2018 Basso (10.1016/j.comcom.2021.01.021_b16) 2010 Rumelhart (10.1016/j.comcom.2021.01.021_b60) 1985 Rezaei (10.1016/j.comcom.2021.01.021_b27) 2019; 57 Pwint (10.1016/j.comcom.2021.01.021_b85) 2019 Nie (10.1016/j.comcom.2021.01.021_b135) 2018; 2018 10.1016/j.comcom.2021.01.021_b158 Sutskever (10.1016/j.comcom.2021.01.021_b66) 2014 Chen (10.1016/j.comcom.2021.01.021_b94) 2017 Chollet (10.1016/j.comcom.2021.01.021_b182) 2015 (10.1016/j.comcom.2021.01.021_b39) 2020 LeCun (10.1016/j.comcom.2021.01.021_b51) 2015; 521 Wang (10.1016/j.comcom.2021.01.021_b96) 2017; 6 Ferreira (10.1016/j.comcom.2021.01.021_b84) 2016; 14 Zhang (10.1016/j.comcom.2021.01.021_b154) 2016 Nguyen (10.1016/j.comcom.2021.01.021_b170) 2018 Li (10.1016/j.comcom.2021.01.021_b108) 2014; 52 Andreoletti (10.1016/j.comcom.2021.01.021_b125) 2019 Labrinidis (10.1016/j.comcom.2021.01.021_b10) 2012; 5 Graves (10.1016/j.comcom.2021.01.021_b64) 2014 Yeo (10.1016/j.comcom.2021.01.021_b97) 2018 Salmi (10.1016/j.comcom.2021.01.021_b59) 2020 Garg (10.1016/j.comcom.2021.01.021_b166) 2019; 16 Hariri (10.1016/j.comcom.2021.01.021_b23) 2019; 6 Wang (10.1016/j.comcom.2021.01.021_b93) 2017 Oliveira (10.1016/j.comcom.2021.01.021_b120) 2016; 3 Oh (10.1016/j.comcom.2021.01.021_b61) 2019 Madan (10.1016/j.comcom.2021.01.021_b124) 2018 Sreekesh (10.1016/j.comcom.2021.01.021_b88) 2016 Mulvey (10.1016/j.comcom.2021.01.021_b145) 2018 Graves (10.1016/j.comcom.2021.01.021_b67) 2013 Torres (10.1016/j.comcom.2021.01.021_b179) 2016 Aceto (10.1016/j.comcom.2021.01.021_b81) 2019; 16 Zhong (10.1016/j.comcom.2021.01.021_b173) 2019; 133 Bkassiny (10.1016/j.comcom.2021.01.021_b184) 2012; 15 Rathore (10.1016/j.comcom.2021.01.021_b24) 2018; 40 Yang (10.1016/j.comcom.2021.01.021_b78) 2019; 10 Masood (10.1016/j.comcom.2021.01.021_b146) 2018 Hochreiter (10.1016/j.comcom.2021.01.021_b63) 1997; 9 Fahlman (10.1016/j.comcom.2021.01.021_b70) 1983 10.1016/j.comcom.2021.01.021_b36 Wang (10.1016/j.comcom.2021.01.021_b100) 2017 Fang (10.1016/j.comcom.2021.01.021_b128) 2018; 5 Rafsanjani (10.1016/j.comcom.2021.01.021_b119) 2014; 244 10.1016/j.comcom.2021.01.021_b34 Jebara (10.1016/j.comcom.2021.01.021_b75) 2012 Oliner (10.1016/j.comcom.2021.01.021_b21) 2011; 9 Boutaba (10.1016/j.comcom.2021.01.021_b9) 2018; 9 Khunteta (10.1016/j.comcom.2021.01.021_b150) 2017 Wang (10.1016/j.comcom.2021.01.021_b188) 2019; 37 Chen (10.1016/j.comcom.2021.01.021_b47) 2014; 2 Li (10.1016/j.comcom.2021.01.021_b136) 2020; 107 Ren (10.1016/j.comcom.2021.01.021_b69) 2015 Naseer (10.1016/j.comcom.2021.01.021_b163) 2018; 6 Gheisari (10.1016/j.comcom.2021.01.021_b26) 2017 Abeshu (10.1016/j.comcom.2021.01.021_b104) 2018; 56 Hu (10.1016/j.comcom.2021.01.021_b189) 2020 Han (10.1016/j.comcom.2021.01.021_b7) 2002 Liu (10.1016/j.comcom.2021.01.021_b50) 2018; 32 Salakhutdinov (10.1016/j.comcom.2021.01.021_b73) 2009 Tong (10.1016/j.comcom.2021.01.021_b99) 2018 So-In (10.1016/j.comcom.2021.01.021_b2) 2009 10.1016/j.comcom.2021.01.021_b138 Wang (10.1016/j.comcom.2021.01.021_b183) 2015; 3 Cecil (10.1016/j.comcom.2021.01.021_b3) 2006 Shu (10.1016/j.comcom.2021.01.021_b118) 2005; 88 Konečnỳ (10.1016/j.comcom.2021.01.021_b79) 2016 Zare Moayedi (10.1016/j.comcom.2021.01.021_b115) 2008 Zhou (10.1016/j.comcom.2021.01.021_b18) 2018; 116 Malaiya (10.1016/j.comcom.2021.01.021_b165) 2018 Liu (10.1016/j.comcom.2021.01.021_b6) 2014; 28 Xu (10.1016/j.comcom.2021.01.021_b110) 2016; 9 Barabas (10.1016/j.comcom.2021.01.021_b122) 2011 Lo’ai (10.1016/j.comcom.2021.01.021_b46) 2016; 4 Iliyasu (10.1016/j.comcom.2021.01.021_b107) 2019; 8 10.1016/j.comcom.2021.01.021_b48 Graves (10.1016/j.comcom.2021.01.021_b65) 2009 10.1016/j.comcom.2021.01.021_b43 Asghar (10.1016/j.comcom.2021.01.021_b153) 2018; 20 Sahay (10.1016/j.comcom.2021.01.021_b91) 2018 Huang (10.1016/j.comcom.2021.01.021_b144) 2020; 58 Assem (10.1016/j.comcom.2021.01.021_b114) 2018 Azari (10.1016/j.comcom.2021.01.021_b116) 2019 Van Efferen (10.1016/j.comcom.2021.01.021_b89) 2017 10.1016/j.comcom.2021.01.021_b140 Masala (10.1016/j.comcom.2021.01.021_b15) 2014 Feng (10.1016/j.comcom.2021.01.021_b126) 2018; 32 Ucci (10.1016/j.comcom.2021.01.021_b28) 2019; 81 Hwang (10.1016/j.comcom.2021.01.021_b45) 2017 Wang (10.1016/j.comcom.2021.01.021_b41) 2013; 29 Alsheikh (10.1016/j.comcom.2021.01.021_b76) 2016; 30 Abduljabbar (10.1016/j.comcom.2021.01.021_b52) 2019; 11 Abadi (10.1016/j.comcom.2021.01.021_b181) 2016 Zhao (10.1016/j.comcom.2021.01.021_b187) 2018; 37 Motamedi (10.1016/j.comcom.2021.01.021_b185) 2017; 16 Wang (10.1016/j.comcom.2021.01.021_b137) 2018; 18 |
| References_xml | – volume: 9 start-page: 16 year: 2018 ident: b9 article-title: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities publication-title: J. Internet Serv. Appl. – volume: 31 start-page: 8021 year: 2019 end-page: 8033 ident: b177 article-title: Deep learning to detect botnet via network flow summaries publication-title: Neural Comput. Appl. – start-page: 173 year: 2017 end-page: 180 ident: b26 article-title: A survey on deep learning in big data publication-title: 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Vol. 2 – start-page: 1 year: 2013 end-page: 6 ident: b86 article-title: Intrusion detection using neuarl networks trained by differential evaluation algorithm publication-title: 2013 10th International ISC Conference on Information Security and Cryptology (ISCISC) – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: b51 article-title: Deep learning publication-title: Nature – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: b63 article-title: Long short-term memory publication-title: Neural Comput. – start-page: 794 year: 2020 end-page: 803 ident: b132 article-title: AZTEC: Anticipatory capacity allocation for zero-touch network slicing publication-title: IEEE INFOCOM 2020-IEEE Conference on Computer Communications – volume: 133 start-page: 151 year: 2019 end-page: 162 ident: b173 article-title: A multi-level deep learning system for malware detection publication-title: Expert Syst. Appl. – reference: Chaoyun Zhang, Paul Patras, Long-term mobile traffic forecasting using deep spatio-temporal neural networks, in: Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2018, pp. 231–240. – year: 2012 ident: b75 article-title: Machine learning: discriminative and generative, Vol. 755 – year: 2016 ident: b181 article-title: Tensorflow: Large-scale machine learning on heterogeneous distributed systems – volume: 6 start-page: 25 year: 2015 ident: b44 article-title: Applications of big data to smart cities publication-title: J. Internet Serv. Appl. – year: 2010 ident: b16 article-title: Rationale, design, and implementation of the network neutrality bot publication-title: Congresso AICA 2010 (L’Aquila) – volume: 18 start-page: 2190 year: 2018 end-page: 2202 ident: b137 article-title: Spatio-temporal analysis and prediction of cellular traffic in metropolis publication-title: IEEE Trans. Mob. Comput. – volume: 2 start-page: 1 year: 2015 ident: b25 article-title: Deep learning applications and challenges in big data analytics publication-title: J. Big Data – volume: 32 start-page: 22 year: 2018 end-page: 27 ident: b103 article-title: An improved stacked auto-encoder for network traffic flow classification publication-title: IEEE Netw. – year: 2019 ident: b13 article-title: An outlier detection method to improve gathered datasets for network behavior analysis in IoT – start-page: 3854 year: 2017 end-page: 3861 ident: b167 article-title: Autoencoder-based feature learning for cyber security applications publication-title: 2017 International Joint Conference on Neural Networks (IJCNN) – year: 2020 ident: b171 article-title: Collaborative intrusion detection for VANETs: A deep learning-based distributed SDN approach publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 16 start-page: 1 year: 2017 end-page: 19 ident: b185 article-title: Machine intelligence on resource-constrained IoT devices: The case of thread granularity optimization for CNN inference publication-title: ACM Trans. Embedded Comput. Syst. (TECS) – reference: Pedro Casas, Alessandro D Alconzo, Tanja Zseby, Marco Mellia, Big-DAMA: big data analytics for network traffic monitoring and analysis, in: Proceedings of the 2016 Workshop on Fostering Latin-American Research in Data Communication Networks, 2016, pp. 1–3. – volume: 13 start-page: 524 year: 2011 end-page: 540 ident: b112 article-title: Green cellular networks: A survey, some research issues and challenges publication-title: IEEE Commun. Surv. Tutor. – reference: Anestis Dalgkitsis, Malamati Louta, George T. Karetsos, Traffic forecasting in cellular networks using the LSTM RNN, in: Proceedings of the 22nd Pan-Hellenic Conference on Informatics, 2018, pp. 28–33. – reference: ITU, global ICT statistics. – volume: 7 start-page: 124514 year: 2019 end-page: 124539 ident: b139 article-title: Cell fault management using machine learning techniques publication-title: IEEE Access – year: 2012 ident: b111 article-title: Cisco visual networking index: Forecast and methodology, 2011–2016, Vol. 518 – start-page: 33 year: 2017 end-page: 48 ident: b149 article-title: Fault detection and localization in distributed systems using recurrent convolutional neural networks publication-title: International Conference on Advanced Data Mining and Applications – start-page: 7 year: 2019 end-page: 14 ident: b151 article-title: On-line error detection and mitigation for time-series data of cyber-physical systems using deep learning based methods publication-title: 2019 15th European Dependable Computing Conference (EDCC) – year: 2017 ident: b8 article-title: Passive flow monitoring of hybrid network connections regarding quality of service parameters for the industrial automation publication-title: 8. Jahreskolloquium “Kommunikation in der Automation–KommA – volume: 6 start-page: 55380 year: 2018 end-page: 55391 ident: b82 article-title: Datanet: Deep learning based encrypted network traffic classification in sdn home gateway publication-title: IEEE Access – start-page: 1 year: 2017 end-page: 7 ident: b4 article-title: Last significant trend change detection method for offline poisson distribution datasets publication-title: 2017 International Symposium on Networks, Computers and Communications (ISNCC) – start-page: 1764 year: 2014 end-page: 1772 ident: b64 article-title: Towards end-to-end speech recognition with recurrent neural networks publication-title: International Conference on Machine Learning – volume: 3 start-page: 1379 year: 2015 end-page: 1391 ident: b183 article-title: Artificial intelligence-based techniques for emerging heterogeneous network: State of the arts, opportunities, and challenges publication-title: IEEE Access – volume: 31 year: 2020 ident: b12 article-title: A comparative node evaluation model for highly heterogeneous massive-scale internet of things-mist networks publication-title: Trans. Emerg. Telecommun. Technol. – volume: 56 start-page: 169 year: 2018 end-page: 175 ident: b104 article-title: Deep learning: The frontier for distributed attack detection in fog-to-things computing publication-title: IEEE Commun. Mag. – start-page: 1271 year: 2017 end-page: 1276 ident: b94 article-title: Seq2img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks publication-title: 2017 IEEE International Conference on Big Data (Big Data) – start-page: 1 year: 2008 end-page: 6 ident: b115 article-title: Arima model for network traffic prediction and anomaly detection publication-title: 2008 International Symposium on Information Technology, Vol. 4 – year: 2002 ident: b55 article-title: Artificial intelligence: a modern approach – volume: 57 start-page: 76 year: 2019 end-page: 81 ident: b27 article-title: Deep learning for encrypted traffic classification: An overview publication-title: IEEE Commun. Mag. – start-page: 141 year: 2019 end-page: 147 ident: b148 article-title: HYPER-VINES: A hybrid learning fault and performance issues eradicator for virtual network services over multi-cloud systems publication-title: 2019 International Conference on Computing, Networking and Communications (ICNC) – volume: 16 start-page: 445 year: 2019 end-page: 458 ident: b81 article-title: Mobile encrypted traffic classification using deep learning: Experimental evaluation, lessons learned, and challenges publication-title: IEEE Trans. Netw. Serv. Manag. – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: b72 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 30 start-page: 22 year: 2016 end-page: 29 ident: b76 article-title: Mobile big data analytics using deep learning and apache spark publication-title: IEEE Netw. – start-page: 1 year: 2018 end-page: 8 ident: b90 article-title: Multilayer perceptron neural network for detection of encrypted vpn network traffic publication-title: 2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA) – start-page: 1 year: 2007 end-page: 5 ident: b117 article-title: Traffic prediction for mobile network using holt-winter’s exponential smoothing publication-title: 2007 15th International Conference on Software, Telecommunications and Computer Networks – volume: 52 start-page: 234 year: 2014 end-page: 240 ident: b108 article-title: The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice publication-title: IEEE Commun. Mag. – start-page: 203 year: 2014 end-page: 212 ident: b15 article-title: Challenges and issues on collecting and analyzing large volumes of network data measurements publication-title: New Trends in Databases and Information Systems – volume: 22 start-page: 1656 year: 2018 end-page: 1659 ident: b133 article-title: Citywide cellular traffic prediction based on densely connected convolutional neural networks publication-title: IEEE Commun. Lett. – volume: 7 start-page: 13546 year: 2019 end-page: 13560 ident: b161 article-title: Introducing deep learning self-adaptive misuse network intrusion detection systems publication-title: IEEE Access – year: 2019 ident: b159 article-title: Official annual cybercrime report – start-page: 95 year: 2011 end-page: 102 ident: b122 article-title: Evaluation of network traffic prediction based on neural networks with multi-task learning and multiresolution decomposition publication-title: 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing – start-page: 246 year: 2019 end-page: 251 ident: b125 article-title: Network traffic prediction based on diffusion convolutional recurrent neural networks publication-title: IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) – start-page: 85 year: 2019 end-page: 89 ident: b102 article-title: Towards unknown traffic identification via embeddings and deep autoencoders publication-title: 2019 26th International Conference on Telecommunications (ICT) – year: 2013 ident: b67 article-title: Generating sequences with recurrent neural networks – start-page: 4125 year: 2016 end-page: 4133 ident: b62 article-title: Memory-efficient backpropagation through time publication-title: Advances in Neural Information Processing Systems – start-page: 545 year: 2009 end-page: 552 ident: b65 article-title: Offline handwriting recognition with multidimensional recurrent neural networks publication-title: Advances in Neural Information Processing Systems – year: 2019 ident: b116 article-title: Cellular traffic prediction and classification: a comparative evaluation of LSTM and ARIMA – volume: 17 start-page: 12 year: 2018 end-page: 22 ident: b178 article-title: N-baiot—network-based detection of iot botnet attacks using deep autoencoders publication-title: IEEE Pervasive Comput. – start-page: 1244 year: 1997 end-page: 1250 ident: b142 article-title: Automated network fault management publication-title: MILCOM 97 MILCOM 97 Proceedings, Vol. 3 – year: 2009 ident: b2 article-title: A survey of network traffic monitoring and analysis tools publication-title: Cse 576m computer system analysis project – start-page: 712 year: 2017 end-page: 717 ident: b100 article-title: Malware traffic classification using convolutional neural network for representation learning publication-title: 2017 International Conference on Information Networking (ICOIN) – start-page: 104 year: 2014 end-page: 112 ident: b22 article-title: Defining architecture components of the big data ecosystem publication-title: 2014 International Conference on Collaboration Technologies and Systems (CTS) – volume: 19 start-page: 1457 year: 2017 end-page: 1477 ident: b31 article-title: A survey on network methodologies for real-time analytics of massive IoT data and open research issues publication-title: IEEE Commun. Surv. Tutor. – year: 2018 ident: b101 article-title: Network traffic anomaly detection using recurrent neural networks – start-page: 280 year: 2019 end-page: 288 ident: b131 article-title: Deepcog: Cognitive network management in sliced 5g networks with deep learning publication-title: IEEE INFOCOM 2019-IEEE Conference on Computer Communications – volume: 4 start-page: 6171 year: 2016 end-page: 6180 ident: b46 article-title: Mobile cloud computing model and big data analysis for healthcare applications publication-title: IEEE Access – volume: 73 start-page: 1192 year: 2017 end-page: 1234 ident: b160 article-title: Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review publication-title: J. Supercomput. – year: 2020 ident: b59 article-title: Stacked boosters network architecture for short term load forecasting in buildings – start-page: 43 year: 2017 end-page: 48 ident: b93 article-title: End-to-end encrypted traffic classification with one-dimensional convolution neural networks publication-title: 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) – start-page: 1457 year: 2018 end-page: 1461 ident: b152 article-title: Deep Q-learning for self-organizing networks fault management and radio performance improvement publication-title: 2018 52nd Asilomar Conference on Signals, Systems, and Computers – year: 2018 ident: b95 article-title: How to achieve high classification accuracy with just a few labels: A semi-supervised approach using sampled packets – volume: 26 start-page: 35 year: 2012 end-page: 40 ident: b80 article-title: Issues and future directions in traffic classification publication-title: IEEE Netw. – start-page: 91 year: 2015 end-page: 99 ident: b69 article-title: Advances in neural information processing systems publication-title: Faster R-CNN: towards real-time object detection with region proposal networks – start-page: 1 year: 2017 end-page: 6 ident: b89 article-title: A multi-layer perceptron approach for flow-based anomaly detection publication-title: 2017 International Symposium on Networks, Computers and Communications (ISNCC) – year: 2020 ident: b186 article-title: Federated learning for resource-constrained IoT devices: Panoramas and state-of-the-art – volume: 9 start-page: 30 year: 2011 end-page: 40 ident: b21 article-title: Advances and challenges in log analysis: Logs contain a wealth of information for help in managing systems. publication-title: Queue – reference: Qing Lyu, Xingjian Lu, Effective Media Traffic Classification Using Deep Learning, in: Proceedings of the 2019 3rd International Conference on Compute and Data Analysis, 2019, pp. 139–146. – start-page: 94 year: 2019 end-page: 99 ident: b87 article-title: Combining best features selection using three classifiers in intrusion detection system publication-title: 2019 International Conference on Advanced Science and Engineering (ICOASE) – start-page: 339 year: 2015 end-page: 344 ident: b106 article-title: Intrusion detection using deep belief networks publication-title: 2015 National Aerospace and Electronics Conference (NAECON) – volume: 88 start-page: 3992 year: 2005 end-page: 3999 ident: b118 article-title: Wireless traffic modeling and prediction using seasonal ARIMA models publication-title: IEICE Trans. Commun. – volume: 5 start-page: 18042 year: 2017 end-page: 18050 ident: b98 article-title: Network traffic classifier with convolutional and recurrent neural networks for internet of things publication-title: IEEE Access – volume: 94 year: 2020 ident: b35 article-title: Boosting algorithms for network intrusion detection: A comparative evaluation of real adaboost, gentle adaboost and modest adaboost publication-title: Eng. Appl. Artif. Intell. – reference: Tijmen Tieleman, Training restricted Boltzmann machines using approximations to the likelihood gradient, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 1064–1071. – volume: 180 year: 2020 ident: b5 article-title: Clustering objectives in wireless sensor networks: A survey and research direction analysis publication-title: Comput. Netw. – start-page: 1291 year: 2016 end-page: 1300 ident: b154 article-title: Automated IT system failure prediction: A deep learning approach publication-title: 2016 IEEE International Conference on Big Data (Big Data) – reference: Justine Sherry, Chang Lan, Raluca Ada Popa, Sylvia Ratnasamy, Blindbox: Deep packet inspection over encrypted traffic, in: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, 2015, pp. 213–226. – year: 2020 ident: b40 article-title: Apache spark – year: 1989 ident: b58 article-title: Generalization and network design strategies. Connectionism in perspective – year: 2020 ident: b14 article-title: A survey and future directions on clustering: From WSNs to IoT and modern networking paradigms publication-title: IEEE Trans. Netw. Serv. Manag. – start-page: 165 year: 2014 end-page: 170 ident: b37 article-title: Large-scale network traffic monitoring with dbstream, a system for rolling big data analysis publication-title: 2014 IEEE International Conference on Big Data (Big Data) – volume: 18 start-page: 3030 year: 2016 end-page: 3051 ident: b147 article-title: Recent advances in fault localization in computer networks publication-title: IEEE Commun. Surv. Tutor. – year: 2020 ident: b189 article-title: Differentially private federated learning for resource-constrained internet of things – volume: 2018 year: 2018 ident: b135 article-title: Network traffic prediction based on deep belief network and spatiotemporal compressive sensing in wireless mesh backbone networks publication-title: Wirel. Commun. Mob. Comput. – volume: 15 start-page: 336 year: 2012 end-page: 361 ident: b157 article-title: A survey of self organisation in future cellular networks publication-title: IEEE Commun. Surv. Tutor. – volume: 32 start-page: 40 year: 2018 end-page: 46 ident: b50 article-title: Urban traffic prediction from mobility data using deep learning publication-title: IEEE Netw. – start-page: 448 year: 2009 end-page: 455 ident: b73 article-title: Deep boltzmann machines publication-title: Artificial Intelligence and Statistics – reference: Haytham Assem, Declan O’Sullivan, Discovering new socio-demographic regional patterns in cities, in: Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, 2016, pp. 1–9. – year: 2015 ident: b182 article-title: Keras – volume: 14 start-page: 404 year: 2016 end-page: 410 ident: b84 article-title: The development and evaluation of a dataset for testing of IDS for wireless networks publication-title: IEEE Latin Amer. Trans. – year: 1983 ident: b70 article-title: Massively parallel architectures for al: NETL, thistle, and Boltzmann machines publication-title: National Conference on Artificial Intelligence, AAAI – volume: 16 start-page: 800 year: 2019 end-page: 813 ident: b1 article-title: A survey on big data for network traffic monitoring and analysis publication-title: IEEE Trans. Netw. Serv. Manag. – volume: 5 start-page: 2032 year: 2012 end-page: 2033 ident: b10 article-title: Challenges and opportunities with big data publication-title: Proc. VLDB Endow. – reference: Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel J Abadi, David J DeWitt, Samuel Madden, Michael Stonebraker, A comparison of approaches to large-scale data analysis, in: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, 2009, pp. 165–178. – volume: 40 start-page: 600 year: 2018 end-page: 610 ident: b24 article-title: Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data publication-title: Sustain. Cities Soc. – volume: 81 start-page: 123 year: 2019 end-page: 147 ident: b28 article-title: Survey of machine learning techniques for malware analysis publication-title: Comput. Secur. – volume: 244 start-page: 514 year: 2014 end-page: 525 ident: b119 article-title: QARIMA: A new approach to prediction in queue theory publication-title: Appl. Math. Comput. – volume: 3 start-page: 28 year: 2016 end-page: 37 ident: b120 article-title: Computer network traffic prediction: a comparison between traditional and deep learning neural networks publication-title: Int. J. Big Data Intell. – reference: Hongyi Zeng, Peyman Kazemian, George Varghese, Nick McKeown, Automatic test packet generation, in: Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies, 2012, pp. 241–252. – volume: 36 start-page: 358 year: 2019 end-page: 367 ident: b53 article-title: Transforming the communication between citizens and government through AI-guided chatbots publication-title: Gov. Inf. Q. – start-page: 42 year: 2016 end-page: 47 ident: b88 article-title: A two-tier network based intrusion detection system architecture using machine learning approach publication-title: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) – start-page: 806 year: 2017 end-page: 811 ident: b150 article-title: Deep learning based link failure mitigation publication-title: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) – start-page: 4 year: 2006 end-page: 7 ident: b3 article-title: A summary of network traffic monitoring and analysis techniques publication-title: Comput. Syst. Anal. – volume: 24 start-page: 1999 year: 2020 end-page: 2012 ident: b49 article-title: Deep packet: A novel approach for encrypted traffic classification using deep learning publication-title: Soft Comput. – reference: Jos van Roosmalen, Harald Vranken, Marko van Eekelen, Applying deep learning on packet flows for botnet detection, in: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018, pp. 1629–1636. – start-page: 1 year: 2009 end-page: 6 ident: b164 article-title: A detailed analysis of the KDD cup 99 data set publication-title: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications – start-page: 5748 year: 2018 end-page: 5756 ident: b54 article-title: The impact of artificial intelligence and virtual personal assistants on marketing publication-title: Encyclopedia of Information Science and Technology, Fourth Edition – volume: 12 start-page: 3039 year: 2017 end-page: 3049 ident: b33 article-title: I know what you saw last minute–encrypted http adaptive video streaming title classification publication-title: IEEE Trans. Inform. Forensics Secur. – start-page: 61 year: 2016 ident: b174 article-title: DL4MD: A deep learning framework for intelligent malware detection publication-title: Proceedings of the International Conference on Data Mining (DMIN) – volume: 6 start-page: 48231 year: 2018 end-page: 48246 ident: b163 article-title: Enhanced network anomaly detection based on deep neural networks publication-title: IEEE Access – start-page: 893 year: 2018 end-page: 898 ident: b165 article-title: An empirical evaluation of deep learning for network anomaly detection publication-title: 2018 International Conference on Computing, Networking and Communications (ICNC) – year: 2017 ident: b45 article-title: Big-data analytics for cloud, IoT and cognitive computing – start-page: 558 year: 1997 end-page: 567 ident: b38 article-title: Web mining: Information and pattern discovery on the world wide web publication-title: Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence – start-page: 222 year: 2019 end-page: 226 ident: b85 article-title: Network traffic anomaly detection based on apache spark publication-title: 2019 International Conference on Advanced Information Technologies (ICAIT) – start-page: 16 year: 2002 end-page: 27 ident: b7 article-title: The architecture of NG-MON: A passive network monitoring system for high-speed IP networks publication-title: International Workshop on Distributed Systems: Operations and Management – volume: 6 start-page: 6556 year: 2019 end-page: 6566 ident: b143 article-title: Machine learning-based link fault identification and localization in complex networks publication-title: IEEE Internet Things J. – year: 2015 ident: b121 article-title: A review of network traffic analysis and prediction techniques – volume: 58 start-page: 40 year: 2020 end-page: 46 ident: b144 article-title: Machine fault detection for intelligent self-driving networks publication-title: IEEE Commun. Mag. – volume: 10 start-page: 1 year: 2019 end-page: 19 ident: b78 article-title: Federated machine learning: Concept and applications publication-title: ACM Trans. Intell. Syst. Technol. (TIST) – year: 2019 ident: b61 article-title: Relaxed weight sharing: Effectively modeling time-varying relationships in clinical time-series – volume: 13 start-page: 42 year: 2006 end-page: 49 ident: b32 article-title: Traffic monitoring and analysis for the optimization of a 3g network publication-title: IEEE Wirel. Commun. – volume: 37 start-page: 1205 year: 2019 end-page: 1221 ident: b188 article-title: Adaptive federated learning in resource constrained edge computing systems publication-title: IEEE J. Sel. Areas Commun. – start-page: 168 year: 2008 end-page: 175 ident: b19 article-title: Dicap: Distributed packet capturing architecture for high-speed network links publication-title: 2008 33rd IEEE Conference on Local Computer Networks (LCN) – volume: 2 start-page: 514 year: 2014 end-page: 525 ident: b47 article-title: Big data deep learning: challenges and perspectives publication-title: IEEE Access – volume: 16 start-page: 924 year: 2019 end-page: 935 ident: b166 article-title: A hybrid deep learning-based model for anomaly detection in cloud datacenter networks publication-title: IEEE Trans. Netw. Serv. Manag. – volume: 15 start-page: 996 year: 2013 end-page: 1019 ident: b156 article-title: Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: A survey publication-title: IEEE Commun. Surv. Tutor. – start-page: 910 year: 2018 end-page: 913 ident: b97 article-title: Flow-based malware detection using convolutional neural network publication-title: 2018 International Conference on Information Networking (ICOIN) – volume: 65 start-page: 84 year: 2014 end-page: 98 ident: b20 article-title: Network monitoring: Present and future publication-title: Comput. Netw. – volume: 14 start-page: 1771 year: 2002 end-page: 1800 ident: b74 article-title: Training products of experts by minimizing contrastive divergence publication-title: Neural Comput. – volume: 29 start-page: 739 year: 2013 end-page: 750 ident: b41 article-title: G-hadoop: Mapreduce across distributed data centers for data-intensive computing publication-title: Future Gener. Comput. Syst. – volume: 8 start-page: 118 year: 2019 end-page: 126 ident: b107 article-title: Semi-supervised encrypted traffic classification with deep convolutional generative adversarial networks publication-title: IEEE Access – volume: 28 start-page: 32 year: 2014 end-page: 39 ident: b6 article-title: Monitoring and analyzing big traffic data of a large-scale cellular network with hadoop publication-title: IEEE Netw. – year: 2020 ident: b39 article-title: Apache hadoop – year: 2016 ident: b56 article-title: Deep Learning – volume: 20 start-page: 2923 year: 2018 end-page: 2960 ident: b77 article-title: Deep learning for IoT big data and streaming analytics: A survey publication-title: IEEE Commun. Surv. Tutor. – volume: 116 start-page: 9 year: 2018 end-page: 23 ident: b18 article-title: A survey on network data collection publication-title: J. Netw. Comput. Appl. – volume: 19 start-page: 2284 year: 2017 end-page: 2321 ident: b141 article-title: A survey on fault management in software-defined networks publication-title: IEEE Commun. Surv. Tutor. – volume: 37 start-page: 2348 year: 2018 end-page: 2359 ident: b187 article-title: Deepthings: Distributed adaptive deep learning inference on resource-constrained IoT edge clusters publication-title: IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. – volume: 9 start-page: 796 year: 2016 end-page: 805 ident: b110 article-title: Big data driven mobile traffic understanding and forecasting: A time series approach publication-title: IEEE Trans. Serv. Comput. – volume: 33 start-page: 36 year: 2019 end-page: 41 ident: b169 article-title: Deep learning for secure mobile edge computing in cyber-physical transportation systems publication-title: IEEE Netw. – year: 2014 ident: b68 article-title: Unifying visual-semantic embeddings with multimodal neural language models – start-page: 1 year: 2017 end-page: 6 ident: b168 article-title: IEEE 802.11 network anomaly detection and attack classification: A deep learning approach publication-title: 2017 IEEE Wireless Communications and Networking Conference (WCNC) – start-page: 1 year: 2018 end-page: 6 ident: b170 article-title: Cyberattack detection in mobile cloud computing: A deep learning approach publication-title: 2018 IEEE Wireless Communications and Networking Conference (WCNC) – volume: 4 start-page: 88 year: 2018 end-page: 95 ident: b175 article-title: Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning publication-title: IEEE Trans. Sustain. Comput. – volume: 88 year: 2020 ident: b92 article-title: A dynamic MLP-based ddos attack detection method using feature selection and feedback publication-title: Comput. Secur. – start-page: 1 year: 2017 end-page: 9 ident: b42 article-title: Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach publication-title: IEEE INFOCOM 2017-IEEE Conference on Computer Communications – volume: 6 start-page: 1792 year: 2017 end-page: 1806 ident: b96 article-title: HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection publication-title: IEEE Access – start-page: 1 year: 2018 end-page: 6 ident: b99 article-title: A novel QUIC traffic classifier based on convolutional neural networks publication-title: 2018 IEEE Global Communications Conference (GLOBECOM) – volume: 5 start-page: 3091 year: 2018 end-page: 3101 ident: b128 article-title: Mobile demand forecasting via deep graph-sequence spatiotemporal modeling in cellular networks publication-title: IEEE Internet Things J. – start-page: 61 year: 2014 end-page: 71 ident: b130 article-title: Multilayer perceptron and stacked autoencoder for internet traffic prediction publication-title: IFIP International Conference on Network and Parallel Computing – volume: 107 year: 2020 ident: b136 article-title: A deep learning method based on an attention mechanism for wireless network traffic prediction publication-title: Ad Hoc Netw. – reference: T. Benson, A. Akella, D. Maltz, Unraveling the complexity of network management, in: Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, ser. NSDI’09, 2009. – year: 1985 ident: b60 article-title: Learning internal representations by error propagation – reference: Ly Vu, Cong Thanh Bui, Quang Uy Nguyen, A deep learning based method for handling imbalanced problem in network traffic classification, in: Proceedings of the Eighth International Symposium on Information and Communication Technology, 2017, pp. 333–339. – start-page: 3104 year: 2014 end-page: 3112 ident: b66 article-title: Sequence to sequence learning with neural networks publication-title: Advances in Neural Information Processing Systems – year: 2016 ident: b79 article-title: Federated learning: Strategies for improving communication efficiency – start-page: 441 year: 2018 end-page: 447 ident: b145 article-title: Cell coverage degradation detection using deep learning techniques publication-title: 2018 International Conference on Information and Communication Technology Convergence (ICTC) – volume: 10 start-page: 122 year: 2019 ident: b155 article-title: A survey of deep learning methods for cyber security publication-title: Information – volume: 28 start-page: 27 year: 2014 end-page: 33 ident: b109 article-title: Challenges in 5G: how to empower SON with big data for enabling 5G publication-title: IEEE Netw. – volume: 1 start-page: 291 year: 1990 ident: b57 article-title: The multilayer perceptron as an approximation to a Bayes optimal discriminant function publication-title: IEEE Trans. Neural Netw. – volume: 32 start-page: 108 year: 2018 end-page: 115 ident: b126 article-title: Deeptp: An end-to-end neural network for mobile cellular traffic prediction publication-title: IEEE Network – volume: 6 start-page: 44 year: 2019 ident: b23 article-title: Uncertainty in big data analytics: survey, opportunities, and challenges publication-title: J. Big Data – start-page: 1 year: 2018 end-page: 6 ident: b162 article-title: Virtual MAC spoofing detection through deep learning publication-title: 2018 IEEE International Conference on Communications (ICC) – volume: 20 start-page: 1682 year: 2018 end-page: 1709 ident: b153 article-title: Self-healing in emerging cellular networks: review, challenges, and research directions publication-title: IEEE Commun. Surv. Tutor. – start-page: 1715 year: 2018 end-page: 1722 ident: b91 article-title: Traffic convergence detection in IoT LLNs: A multilayer perceptron based mechanism publication-title: 2018 IEEE Symposium Series on Computational Intelligence (SSCI) – start-page: 1 year: 2017 end-page: 6 ident: b113 article-title: A study of deep learning networks on mobile traffic forecasting publication-title: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) – volume: 41 start-page: 134 year: 2011 end-page: 145 ident: b17 article-title: Broadband internet performance: a view from the gateway publication-title: ACM SIGCOMM Comput. Commun. Rev. – year: 2017 ident: b29 article-title: The dark side(-channel) of mobile devices: A survey on network traffic analysis – volume: 19 start-page: 2432 year: 2017 end-page: 2455 ident: b30 article-title: State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems publication-title: IEEE Commun. Surv. Tutor. – start-page: 206 year: 2018 end-page: 212 ident: b146 article-title: Deep learning based detection of sleeping cells in next generation cellular networks publication-title: 2018 IEEE Global Communications Conference (GLOBECOM) – start-page: 222 year: 2018 end-page: 237 ident: b114 article-title: St-dennetfus: A new deep learning approach for network demand prediction publication-title: Joint European Conference on Machine Learning and Knowledge Discovery in Databases – reference: Terran Lane, Carla E. Brodley, An application of machine learning to anomaly detection, in: Proceedings of the 20th National Information Systems Security Conference, Vol. 377, Baltimore, USA, 1997, pp. 366–380. – start-page: 1 year: 2016 end-page: 6 ident: b179 article-title: An analysis of recurrent neural networks for botnet detection behavior publication-title: 2016 IEEE Biennial Congress of Argentina (ARGENCON) – volume: 15 start-page: 1136 year: 2012 end-page: 1159 ident: b184 article-title: A survey on machine-learning techniques in cognitive radios publication-title: IEEE Commu. Surv. Tutor. – volume: 6 start-page: 5276 year: 2017 end-page: 5289 ident: b134 article-title: Interactive temporal recurrent convolution network for traffic prediction in data centers publication-title: IEEE Access – reference: Mahmoud Abbasi, Amin Shahraki, Hamid R Barzegar, Claus Pahl, Synchronization Techniques in “Device to Device-and Vehicle to Vehicle-Enabled” Cellular Networks: A survey, Comput. Electr. Eng., 90 106955. – start-page: 1 year: 2018 end-page: 5 ident: b124 article-title: Predicting computer network traffic: a time series forecasting approach using DWT, ARIMA and RNN publication-title: 2018 Eleventh International Conference on Contemporary Computing (IC3) – volume: 70 start-page: 263 year: 2017 end-page: 286 ident: b11 article-title: Critical analysis of big data challenges and analytical methods publication-title: J. Bus. Res. – year: 2019 ident: b180 article-title: A survey on data collection for machine learning: a big data-ai integration perspective publication-title: IEEE Trans. Knowl. Data Eng. – volume: 11 start-page: 189 year: 2019 ident: b52 article-title: Applications of artificial intelligence in transport: An overview publication-title: Sustainability – year: 2018 ident: 10.1016/j.comcom.2021.01.021_b95 – volume: 10 start-page: 122 issue: 4 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b155 article-title: A survey of deep learning methods for cyber security publication-title: Information doi: 10.3390/info10040122 – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 10.1016/j.comcom.2021.01.021_b72 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – start-page: 893 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b165 article-title: An empirical evaluation of deep learning for network anomaly detection – volume: 14 start-page: 1771 issue: 8 year: 2002 ident: 10.1016/j.comcom.2021.01.021_b74 article-title: Training products of experts by minimizing contrastive divergence publication-title: Neural Comput. doi: 10.1162/089976602760128018 – start-page: 1 year: 2009 ident: 10.1016/j.comcom.2021.01.021_b164 article-title: A detailed analysis of the KDD cup 99 data set – start-page: 16 year: 2002 ident: 10.1016/j.comcom.2021.01.021_b7 article-title: The architecture of NG-MON: A passive network monitoring system for high-speed IP networks – start-page: 806 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b150 article-title: Deep learning based link failure mitigation – start-page: 4 year: 2006 ident: 10.1016/j.comcom.2021.01.021_b3 article-title: A summary of network traffic monitoring and analysis techniques publication-title: Comput. Syst. Anal. – volume: 4 start-page: 88 issue: 1 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b175 article-title: Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning publication-title: IEEE Trans. Sustain. Comput. doi: 10.1109/TSUSC.2018.2809665 – volume: 5 start-page: 2032 issue: 12 year: 2012 ident: 10.1016/j.comcom.2021.01.021_b10 article-title: Challenges and opportunities with big data publication-title: Proc. VLDB Endow. doi: 10.14778/2367502.2367572 – start-page: 173 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b26 article-title: A survey on deep learning in big data – year: 2002 ident: 10.1016/j.comcom.2021.01.021_b55 – volume: 16 start-page: 924 issue: 3 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b166 article-title: A hybrid deep learning-based model for anomaly detection in cloud datacenter networks publication-title: IEEE Trans. Netw. Serv. Manag. doi: 10.1109/TNSM.2019.2927886 – volume: 244 start-page: 514 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b119 article-title: QARIMA: A new approach to prediction in queue theory publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2014.06.108 – volume: 31 issue: 12 year: 2020 ident: 10.1016/j.comcom.2021.01.021_b12 article-title: A comparative node evaluation model for highly heterogeneous massive-scale internet of things-mist networks publication-title: Trans. Emerg. Telecommun. Technol. – volume: 12 start-page: 3039 issue: 12 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b33 article-title: I know what you saw last minute–encrypted http adaptive video streaming title classification publication-title: IEEE Trans. Inform. Forensics Secur. doi: 10.1109/TIFS.2017.2730819 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 10.1016/j.comcom.2021.01.021_b63 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – ident: 10.1016/j.comcom.2021.01.021_b105 doi: 10.1145/3155133.3155175 – year: 2017 ident: 10.1016/j.comcom.2021.01.021_b29 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.comcom.2021.01.021_b51 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 1 start-page: 291 issue: 4 year: 1990 ident: 10.1016/j.comcom.2021.01.021_b57 article-title: The multilayer perceptron as an approximation to a Bayes optimal discriminant function publication-title: IEEE Trans. Neural Netw. – start-page: 1457 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b152 article-title: Deep Q-learning for self-organizing networks fault management and radio performance improvement – year: 2020 ident: 10.1016/j.comcom.2021.01.021_b14 article-title: A survey and future directions on clustering: From WSNs to IoT and modern networking paradigms publication-title: IEEE Trans. Netw. Serv. Manag. doi: 10.1109/TNSM.2020.3035315 – start-page: 168 year: 2008 ident: 10.1016/j.comcom.2021.01.021_b19 article-title: Dicap: Distributed packet capturing architecture for high-speed network links – start-page: 1291 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b154 article-title: Automated IT system failure prediction: A deep learning approach – start-page: 1271 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b94 article-title: Seq2img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks – start-page: 1 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b168 article-title: IEEE 802.11 network anomaly detection and attack classification: A deep learning approach – start-page: 222 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b114 article-title: St-dennetfus: A new deep learning approach for network demand prediction – ident: 10.1016/j.comcom.2021.01.021_b48 doi: 10.1145/2940116.2940117 – year: 2012 ident: 10.1016/j.comcom.2021.01.021_b111 – start-page: 1715 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b91 article-title: Traffic convergence detection in IoT LLNs: A multilayer perceptron based mechanism – volume: 3 start-page: 1379 year: 2015 ident: 10.1016/j.comcom.2021.01.021_b183 article-title: Artificial intelligence-based techniques for emerging heterogeneous network: State of the arts, opportunities, and challenges publication-title: IEEE Access doi: 10.1109/ACCESS.2015.2467174 – volume: 9 start-page: 796 issue: 5 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b110 article-title: Big data driven mobile traffic understanding and forecasting: A time series approach publication-title: IEEE Trans. Serv. Comput. doi: 10.1109/TSC.2016.2599878 – volume: 18 start-page: 2190 issue: 9 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b137 article-title: Spatio-temporal analysis and prediction of cellular traffic in metropolis publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2018.2870135 – year: 1989 ident: 10.1016/j.comcom.2021.01.021_b58 – volume: 13 start-page: 524 issue: 4 year: 2011 ident: 10.1016/j.comcom.2021.01.021_b112 article-title: Green cellular networks: A survey, some research issues and challenges publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/SURV.2011.092311.00031 – volume: 15 start-page: 996 issue: 3 year: 2013 ident: 10.1016/j.comcom.2021.01.021_b156 article-title: Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: A survey publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/SURV.2013.052213.00000 – volume: 33 start-page: 36 issue: 4 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b169 article-title: Deep learning for secure mobile edge computing in cyber-physical transportation systems publication-title: IEEE Netw. doi: 10.1109/MNET.2019.1800458 – start-page: 95 year: 2011 ident: 10.1016/j.comcom.2021.01.021_b122 article-title: Evaluation of network traffic prediction based on neural networks with multi-task learning and multiresolution decomposition – volume: 29 start-page: 739 issue: 3 year: 2013 ident: 10.1016/j.comcom.2021.01.021_b41 article-title: G-hadoop: Mapreduce across distributed data centers for data-intensive computing publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2012.09.001 – volume: 20 start-page: 1682 issue: 3 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b153 article-title: Self-healing in emerging cellular networks: review, challenges, and research directions publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2018.2825786 – start-page: 165 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b37 article-title: Large-scale network traffic monitoring with dbstream, a system for rolling big data analysis – ident: 10.1016/j.comcom.2021.01.021_b34 doi: 10.1145/1559845.1559865 – ident: 10.1016/j.comcom.2021.01.021_b123 doi: 10.1145/3021304.3021305 – year: 2013 ident: 10.1016/j.comcom.2021.01.021_b67 – year: 2016 ident: 10.1016/j.comcom.2021.01.021_b181 – volume: 16 start-page: 800 issue: 3 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b1 article-title: A survey on big data for network traffic monitoring and analysis publication-title: IEEE Trans. Netw. Serv. Manag. doi: 10.1109/TNSM.2019.2933358 – start-page: 712 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b100 article-title: Malware traffic classification using convolutional neural network for representation learning – volume: 28 start-page: 27 issue: 6 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b109 article-title: Challenges in 5G: how to empower SON with big data for enabling 5G publication-title: IEEE Netw. doi: 10.1109/MNET.2014.6963801 – ident: 10.1016/j.comcom.2021.01.021_b158 – start-page: 203 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b15 article-title: Challenges and issues on collecting and analyzing large volumes of network data measurements – volume: 2018 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b135 article-title: Network traffic prediction based on deep belief network and spatiotemporal compressive sensing in wireless mesh backbone networks publication-title: Wirel. Commun. Mob. Comput. doi: 10.1155/2018/1260860 – volume: 65 start-page: 84 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b20 article-title: Network monitoring: Present and future publication-title: Comput. Netw. doi: 10.1016/j.comnet.2014.03.007 – volume: 28 start-page: 32 issue: 4 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b6 article-title: Monitoring and analyzing big traffic data of a large-scale cellular network with hadoop publication-title: IEEE Netw. doi: 10.1109/MNET.2014.6863129 – start-page: 1 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b89 article-title: A multi-layer perceptron approach for flow-based anomaly detection – volume: 17 start-page: 12 issue: 3 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b178 article-title: N-baiot—network-based detection of iot botnet attacks using deep autoencoders publication-title: IEEE Pervasive Comput. doi: 10.1109/MPRV.2018.03367731 – start-page: 1 year: 2013 ident: 10.1016/j.comcom.2021.01.021_b86 article-title: Intrusion detection using neuarl networks trained by differential evaluation algorithm – volume: 32 start-page: 22 issue: 6 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b103 article-title: An improved stacked auto-encoder for network traffic flow classification publication-title: IEEE Netw. doi: 10.1109/MNET.2018.1800078 – volume: 2 start-page: 514 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b47 article-title: Big data deep learning: challenges and perspectives publication-title: IEEE Access doi: 10.1109/ACCESS.2014.2325029 – volume: 20 start-page: 2923 issue: 4 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b77 article-title: Deep learning for IoT big data and streaming analytics: A survey publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2018.2844341 – start-page: 1 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b4 article-title: Last significant trend change detection method for offline poisson distribution datasets – volume: 4 start-page: 6171 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b46 article-title: Mobile cloud computing model and big data analysis for healthcare applications publication-title: IEEE Access doi: 10.1109/ACCESS.2016.2613278 – volume: 7 start-page: 13546 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b161 article-title: Introducing deep learning self-adaptive misuse network intrusion detection systems publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2893871 – volume: 9 start-page: 16 issue: 1 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b9 article-title: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities publication-title: J. Internet Serv. Appl. doi: 10.1186/s13174-018-0087-2 – year: 2019 ident: 10.1016/j.comcom.2021.01.021_b61 – volume: 19 start-page: 1457 issue: 3 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b31 article-title: A survey on network methodologies for real-time analytics of massive IoT data and open research issues publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2017.2694469 – start-page: 448 year: 2009 ident: 10.1016/j.comcom.2021.01.021_b73 article-title: Deep boltzmann machines – year: 2016 ident: 10.1016/j.comcom.2021.01.021_b56 – volume: 40 start-page: 600 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b24 article-title: Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data publication-title: Sustain. Cities Soc. doi: 10.1016/j.scs.2017.12.022 – volume: 13 start-page: 42 issue: 6 year: 2006 ident: 10.1016/j.comcom.2021.01.021_b32 article-title: Traffic monitoring and analysis for the optimization of a 3g network publication-title: IEEE Wirel. Commun. doi: 10.1109/MWC.2006.275197 – start-page: 1 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b124 article-title: Predicting computer network traffic: a time series forecasting approach using DWT, ARIMA and RNN – start-page: 91 year: 2015 ident: 10.1016/j.comcom.2021.01.021_b69 article-title: Advances in neural information processing systems – start-page: 33 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b149 article-title: Fault detection and localization in distributed systems using recurrent convolutional neural networks – volume: 7 start-page: 124514 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b139 article-title: Cell fault management using machine learning techniques publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2938410 – start-page: 910 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b97 article-title: Flow-based malware detection using convolutional neural network – year: 2009 ident: 10.1016/j.comcom.2021.01.021_b2 article-title: A survey of network traffic monitoring and analysis tools – volume: 133 start-page: 151 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b173 article-title: A multi-level deep learning system for malware detection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.04.064 – volume: 24 start-page: 1999 issue: 3 year: 2020 ident: 10.1016/j.comcom.2021.01.021_b49 article-title: Deep packet: A novel approach for encrypted traffic classification using deep learning publication-title: Soft Comput. doi: 10.1007/s00500-019-04030-2 – year: 2020 ident: 10.1016/j.comcom.2021.01.021_b171 article-title: Collaborative intrusion detection for VANETs: A deep learning-based distributed SDN approach publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2020.3027390 – year: 2010 ident: 10.1016/j.comcom.2021.01.021_b16 article-title: Rationale, design, and implementation of the network neutrality bot – volume: 10 start-page: 1 issue: 2 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b78 article-title: Federated machine learning: Concept and applications publication-title: ACM Trans. Intell. Syst. Technol. (TIST) doi: 10.1145/3298981 – volume: 9 start-page: 30 issue: 12 year: 2011 ident: 10.1016/j.comcom.2021.01.021_b21 article-title: Advances and challenges in log analysis: Logs contain a wealth of information for help in managing systems. publication-title: Queue doi: 10.1145/2076796.2082137 – year: 2020 ident: 10.1016/j.comcom.2021.01.021_b40 – start-page: 1 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b179 article-title: An analysis of recurrent neural networks for botnet detection behavior – volume: 88 year: 2020 ident: 10.1016/j.comcom.2021.01.021_b92 article-title: A dynamic MLP-based ddos attack detection method using feature selection and feedback publication-title: Comput. Secur. doi: 10.1016/j.cose.2019.101645 – volume: 15 start-page: 336 issue: 1 year: 2012 ident: 10.1016/j.comcom.2021.01.021_b157 article-title: A survey of self organisation in future cellular networks publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/SURV.2012.021312.00116 – volume: 19 start-page: 2432 issue: 4 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b30 article-title: State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2017.2707140 – volume: 14 start-page: 404 issue: 1 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b84 article-title: The development and evaluation of a dataset for testing of IDS for wireless networks publication-title: IEEE Latin Amer. Trans. doi: 10.1109/TLA.2016.7430108 – start-page: 1 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b90 article-title: Multilayer perceptron neural network for detection of encrypted vpn network traffic – start-page: 222 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b85 article-title: Network traffic anomaly detection based on apache spark – volume: 6 start-page: 25 issue: 1 year: 2015 ident: 10.1016/j.comcom.2021.01.021_b44 article-title: Applications of big data to smart cities publication-title: J. Internet Serv. Appl. doi: 10.1186/s13174-015-0041-5 – volume: 41 start-page: 134 issue: 4 year: 2011 ident: 10.1016/j.comcom.2021.01.021_b17 article-title: Broadband internet performance: a view from the gateway publication-title: ACM SIGCOMM Comput. Commun. Rev. doi: 10.1145/2043164.2018452 – volume: 30 start-page: 22 issue: 3 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b76 article-title: Mobile big data analytics using deep learning and apache spark publication-title: IEEE Netw. doi: 10.1109/MNET.2016.7474340 – start-page: 558 year: 1997 ident: 10.1016/j.comcom.2021.01.021_b38 article-title: Web mining: Information and pattern discovery on the world wide web – volume: 11 start-page: 189 issue: 1 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b52 article-title: Applications of artificial intelligence in transport: An overview publication-title: Sustainability doi: 10.3390/su11010189 – start-page: 43 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b93 article-title: End-to-end encrypted traffic classification with one-dimensional convolution neural networks – start-page: 280 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b131 article-title: Deepcog: Cognitive network management in sliced 5g networks with deep learning – volume: 16 start-page: 1 issue: 5s year: 2017 ident: 10.1016/j.comcom.2021.01.021_b185 article-title: Machine intelligence on resource-constrained IoT devices: The case of thread granularity optimization for CNN inference publication-title: ACM Trans. Embedded Comput. Syst. (TECS) doi: 10.1145/3126555 – volume: 6 start-page: 48231 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b163 article-title: Enhanced network anomaly detection based on deep neural networks publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2863036 – volume: 37 start-page: 1205 issue: 6 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b188 article-title: Adaptive federated learning in resource constrained edge computing systems publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2019.2904348 – ident: 10.1016/j.comcom.2021.01.021_b43 doi: 10.1145/2785956.2787502 – volume: 26 start-page: 35 issue: 1 year: 2012 ident: 10.1016/j.comcom.2021.01.021_b80 article-title: Issues and future directions in traffic classification publication-title: IEEE Netw. doi: 10.1109/MNET.2012.6135854 – start-page: 85 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b102 article-title: Towards unknown traffic identification via embeddings and deep autoencoders – volume: 15 start-page: 1136 issue: 3 year: 2012 ident: 10.1016/j.comcom.2021.01.021_b184 article-title: A survey on machine-learning techniques in cognitive radios publication-title: IEEE Commu. Surv. Tutor. doi: 10.1109/SURV.2012.100412.00017 – ident: 10.1016/j.comcom.2021.01.021_b138 doi: 10.1145/2413176.2413205 – volume: 88 start-page: 3992 issue: 10 year: 2005 ident: 10.1016/j.comcom.2021.01.021_b118 article-title: Wireless traffic modeling and prediction using seasonal ARIMA models publication-title: IEICE Trans. Commun. doi: 10.1093/ietcom/e88-b.10.3992 – start-page: 1 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b113 article-title: A study of deep learning networks on mobile traffic forecasting – year: 2020 ident: 10.1016/j.comcom.2021.01.021_b186 – start-page: 7 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b151 article-title: On-line error detection and mitigation for time-series data of cyber-physical systems using deep learning based methods – volume: 36 start-page: 358 issue: 2 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b53 article-title: Transforming the communication between citizens and government through AI-guided chatbots publication-title: Gov. Inf. Q. doi: 10.1016/j.giq.2018.10.001 – volume: 19 start-page: 2284 issue: 4 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b141 article-title: A survey on fault management in software-defined networks publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2017.2719862 – start-page: 1764 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b64 article-title: Towards end-to-end speech recognition with recurrent neural networks – year: 2017 ident: 10.1016/j.comcom.2021.01.021_b8 article-title: Passive flow monitoring of hybrid network connections regarding quality of service parameters for the industrial automation – start-page: 1244 year: 1997 ident: 10.1016/j.comcom.2021.01.021_b142 article-title: Automated network fault management – ident: 10.1016/j.comcom.2021.01.021_b36 – start-page: 5748 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b54 article-title: The impact of artificial intelligence and virtual personal assistants on marketing – start-page: 3104 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b66 article-title: Sequence to sequence learning with neural networks – start-page: 94 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b87 article-title: Combining best features selection using three classifiers in intrusion detection system – ident: 10.1016/j.comcom.2021.01.021_b129 doi: 10.1145/3209582.3209606 – ident: 10.1016/j.comcom.2021.01.021_b176 doi: 10.1145/3167132.3167306 – ident: 10.1016/j.comcom.2021.01.021_b172 doi: 10.1016/j.compeleceng.2020.106955 – year: 2014 ident: 10.1016/j.comcom.2021.01.021_b68 – year: 2016 ident: 10.1016/j.comcom.2021.01.021_b79 – volume: 6 start-page: 55380 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b82 article-title: Datanet: Deep learning based encrypted network traffic classification in sdn home gateway publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2872430 – volume: 57 start-page: 76 issue: 5 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b27 article-title: Deep learning for encrypted traffic classification: An overview publication-title: IEEE Commun. Mag. doi: 10.1109/MCOM.2019.1800819 – volume: 107 year: 2020 ident: 10.1016/j.comcom.2021.01.021_b136 article-title: A deep learning method based on an attention mechanism for wireless network traffic prediction publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2020.102258 – volume: 5 start-page: 18042 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b98 article-title: Network traffic classifier with convolutional and recurrent neural networks for internet of things publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2747560 – volume: 58 start-page: 40 issue: 1 year: 2020 ident: 10.1016/j.comcom.2021.01.021_b144 article-title: Machine fault detection for intelligent self-driving networks publication-title: IEEE Commun. Mag. doi: 10.1109/MCOM.001.1900283 – start-page: 206 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b146 article-title: Deep learning based detection of sleeping cells in next generation cellular networks – start-page: 545 year: 2009 ident: 10.1016/j.comcom.2021.01.021_b65 article-title: Offline handwriting recognition with multidimensional recurrent neural networks – year: 2019 ident: 10.1016/j.comcom.2021.01.021_b159 – year: 1983 ident: 10.1016/j.comcom.2021.01.021_b70 article-title: Massively parallel architectures for al: NETL, thistle, and Boltzmann machines – start-page: 61 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b174 article-title: DL4MD: A deep learning framework for intelligent malware detection – volume: 6 start-page: 5276 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b134 article-title: Interactive temporal recurrent convolution network for traffic prediction in data centers publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2787696 – volume: 3 start-page: 28 issue: 1 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b120 article-title: Computer network traffic prediction: a comparison between traditional and deep learning neural networks publication-title: Int. J. Big Data Intell. doi: 10.1504/IJBDI.2016.073903 – year: 2015 ident: 10.1016/j.comcom.2021.01.021_b121 – volume: 5 start-page: 3091 issue: 4 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b128 article-title: Mobile demand forecasting via deep graph-sequence spatiotemporal modeling in cellular networks publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2832071 – volume: 22 start-page: 1656 issue: 8 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b133 article-title: Citywide cellular traffic prediction based on densely connected convolutional neural networks publication-title: IEEE Commun. Lett. doi: 10.1109/LCOMM.2018.2841832 – volume: 180 year: 2020 ident: 10.1016/j.comcom.2021.01.021_b5 article-title: Clustering objectives in wireless sensor networks: A survey and research direction analysis publication-title: Comput. Netw. doi: 10.1016/j.comnet.2020.107376 – volume: 73 start-page: 1192 issue: 3 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b160 article-title: Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review publication-title: J. Supercomput. doi: 10.1007/s11227-016-1805-9 – volume: 32 start-page: 40 issue: 4 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b50 article-title: Urban traffic prediction from mobility data using deep learning publication-title: IEEE Netw. doi: 10.1109/MNET.2018.1700411 – year: 2019 ident: 10.1016/j.comcom.2021.01.021_b116 – start-page: 441 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b145 article-title: Cell coverage degradation detection using deep learning techniques – volume: 116 start-page: 9 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b18 article-title: A survey on network data collection publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2018.05.004 – volume: 31 start-page: 8021 issue: 11 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b177 article-title: Deep learning to detect botnet via network flow summaries publication-title: Neural Comput. Appl. doi: 10.1007/s00521-018-3595-x – volume: 70 start-page: 263 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b11 article-title: Critical analysis of big data challenges and analytical methods publication-title: J. Bus. Res. doi: 10.1016/j.jbusres.2016.08.001 – ident: 10.1016/j.comcom.2021.01.021_b83 doi: 10.1145/3314545.3316278 – year: 2017 ident: 10.1016/j.comcom.2021.01.021_b45 – start-page: 794 year: 2020 ident: 10.1016/j.comcom.2021.01.021_b132 article-title: AZTEC: Anticipatory capacity allocation for zero-touch network slicing – start-page: 1 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b42 article-title: Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach – volume: 6 start-page: 1792 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b96 article-title: HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2780250 – ident: 10.1016/j.comcom.2021.01.021_b140 – volume: 52 start-page: 234 issue: 6 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b108 article-title: The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice publication-title: IEEE Commun. Mag. doi: 10.1109/MCOM.2014.6829969 – volume: 6 start-page: 6556 issue: 4 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b143 article-title: Machine learning-based link fault identification and localization in complex networks publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2019.2908019 – start-page: 1 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b170 article-title: Cyberattack detection in mobile cloud computing: A deep learning approach – volume: 81 start-page: 123 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b28 article-title: Survey of machine learning techniques for malware analysis publication-title: Comput. Secur. doi: 10.1016/j.cose.2018.11.001 – start-page: 61 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b130 article-title: Multilayer perceptron and stacked autoencoder for internet traffic prediction – year: 2012 ident: 10.1016/j.comcom.2021.01.021_b75 – start-page: 42 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b88 article-title: A two-tier network based intrusion detection system architecture using machine learning approach – year: 2020 ident: 10.1016/j.comcom.2021.01.021_b39 – volume: 94 year: 2020 ident: 10.1016/j.comcom.2021.01.021_b35 article-title: Boosting algorithms for network intrusion detection: A comparative evaluation of real adaboost, gentle adaboost and modest adaboost publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2020.103770 – year: 2020 ident: 10.1016/j.comcom.2021.01.021_b189 – volume: 56 start-page: 169 issue: 2 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b104 article-title: Deep learning: The frontier for distributed attack detection in fog-to-things computing publication-title: IEEE Commun. Mag. doi: 10.1109/MCOM.2018.1700332 – year: 2020 ident: 10.1016/j.comcom.2021.01.021_b59 – volume: 8 start-page: 118 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b107 article-title: Semi-supervised encrypted traffic classification with deep convolutional generative adversarial networks publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2962106 – start-page: 246 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b125 article-title: Network traffic prediction based on diffusion convolutional recurrent neural networks – ident: 10.1016/j.comcom.2021.01.021_b71 doi: 10.1145/1390156.1390290 – volume: 16 start-page: 445 issue: 2 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b81 article-title: Mobile encrypted traffic classification using deep learning: Experimental evaluation, lessons learned, and challenges publication-title: IEEE Trans. Netw. Serv. Manag. doi: 10.1109/TNSM.2019.2899085 – year: 1985 ident: 10.1016/j.comcom.2021.01.021_b60 – year: 2018 ident: 10.1016/j.comcom.2021.01.021_b101 – start-page: 339 year: 2015 ident: 10.1016/j.comcom.2021.01.021_b106 article-title: Intrusion detection using deep belief networks – volume: 2 start-page: 1 issue: 1 year: 2015 ident: 10.1016/j.comcom.2021.01.021_b25 article-title: Deep learning applications and challenges in big data analytics publication-title: J. Big Data doi: 10.1186/s40537-014-0007-7 – year: 2019 ident: 10.1016/j.comcom.2021.01.021_b13 – start-page: 104 year: 2014 ident: 10.1016/j.comcom.2021.01.021_b22 article-title: Defining architecture components of the big data ecosystem – start-page: 1 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b99 article-title: A novel QUIC traffic classifier based on convolutional neural networks – volume: 32 start-page: 108 issue: 6 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b126 article-title: Deeptp: An end-to-end neural network for mobile cellular traffic prediction publication-title: IEEE Network doi: 10.1109/MNET.2018.1800127 – volume: 37 start-page: 2348 issue: 11 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b187 article-title: Deepthings: Distributed adaptive deep learning inference on resource-constrained IoT edge clusters publication-title: IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. doi: 10.1109/TCAD.2018.2858384 – ident: 10.1016/j.comcom.2021.01.021_b127 doi: 10.1145/3291533.3291540 – start-page: 141 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b148 article-title: HYPER-VINES: A hybrid learning fault and performance issues eradicator for virtual network services over multi-cloud systems – year: 2019 ident: 10.1016/j.comcom.2021.01.021_b180 article-title: A survey on data collection for machine learning: a big data-ai integration perspective publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2019.2946162 – start-page: 3854 year: 2017 ident: 10.1016/j.comcom.2021.01.021_b167 article-title: Autoencoder-based feature learning for cyber security applications – volume: 6 start-page: 44 issue: 1 year: 2019 ident: 10.1016/j.comcom.2021.01.021_b23 article-title: Uncertainty in big data analytics: survey, opportunities, and challenges publication-title: J. Big Data doi: 10.1186/s40537-019-0206-3 – start-page: 1 year: 2007 ident: 10.1016/j.comcom.2021.01.021_b117 article-title: Traffic prediction for mobile network using holt-winter’s exponential smoothing – volume: 18 start-page: 3030 issue: 4 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b147 article-title: Recent advances in fault localization in computer networks publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2016.2570599 – start-page: 1 year: 2018 ident: 10.1016/j.comcom.2021.01.021_b162 article-title: Virtual MAC spoofing detection through deep learning – start-page: 1 year: 2008 ident: 10.1016/j.comcom.2021.01.021_b115 article-title: Arima model for network traffic prediction and anomaly detection – start-page: 4125 year: 2016 ident: 10.1016/j.comcom.2021.01.021_b62 article-title: Memory-efficient backpropagation through time – year: 2015 ident: 10.1016/j.comcom.2021.01.021_b182 |
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