A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection
The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Tr...
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| Published in | PloS one Vol. 10; no. 10; p. e0139301 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
05.10.2015
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0139301 |
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| Abstract | The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms. |
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| AbstractList | The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms. The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms.The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms. |
| Audience | Academic |
| Author | Lin, Yi-Shan Lee, Chun-Liang Chen, Yaw-Chung |
| AuthorAffiliation | 1 Department of Computer Science and Information Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Taoyuan, Taiwan 2 Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan Universitat Rovira i Virgili, SPAIN |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26437335$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_28948_ngumuh_1184020 crossref_primary_10_1371_journal_pone_0163535 crossref_primary_10_3390_a15070237 crossref_primary_10_1109_TDSC_2016_2593442 crossref_primary_10_1002_ett_3711 crossref_primary_10_1109_ACCESS_2020_2969038 crossref_primary_10_3390_a10010016 crossref_primary_10_3390_app13148104 crossref_primary_10_1007_s11227_022_04673_3 crossref_primary_10_3390_a10020058 |
| Cites_doi | 10.1145/2046707.2046741 10.1145/2436256.2436271 10.1137/0206024 10.1371/journal.pone.0120976 10.1145/1244002.1244259 10.1145/1366230.1366237 10.1007/s10207-008-0061-2 10.1016/j.cose.2008.06.001 10.1109/FCCM.2004.46 10.1007/978-3-540-74320-0_6 10.1145/974043.974078 10.1109/ACSAC.2006.35 10.1145/1015047.1015055 10.1145/359842.359859 10.1016/S1389-1286(99)00112-7 10.1007/978-3-540-45234-8_94 10.1145/1400181.1400197 10.1016/B978-012088476-6/50007-1 10.1109/ISPASS.2008.4510736 10.1145/360825.360855 10.1007/978-3-540-87403-4_7 10.1145/1365490.1365500 10.1109/WAINA.2008.145 |
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| Copyright | COPYRIGHT 2015 Public Library of Science 2015 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2015 Lee et al 2015 Lee et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Current Address: No. 1001, University Road, Hsinchu 30010, Taiwan Current Address: No. 259, Wen-Hwa 1st Road, Kwei-Shan, Taoyuan 33302, Taiwan Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: CLL YSL. Performed the experiments: YSL. Analyzed the data: CLL YSL. Contributed reagents/materials/analysis tools: CLL YSL. Wrote the paper: CLL YSL YCC. Proposed the algorithm and revised the manuscript: CLL. |
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| SubjectTerms | Algorithms Architectural engineering Automatic Data Processing - methods Central processing units Computer engineering Computer Graphics Computer science Computer Security CPUs CPUs (Central processing units) Cybersecurity Data encryption Energy efficiency Field programmable gate arrays Filtration Graphics processing units Inspection Internet Intrusion detection systems Multiprocessing Network security Parallel processing Pattern matching Pattern Recognition, Automated - methods Security Servers Software Traffic speed |
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| Title | A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/26437335 https://www.proquest.com/docview/1719374559 https://www.proquest.com/docview/1719977048 https://pubmed.ncbi.nlm.nih.gov/PMC4593550 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0139301&type=printable https://doaj.org/article/20de20e14e874a1e95cabd6e0ff7c157 http://dx.doi.org/10.1371/journal.pone.0139301 |
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