Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment
There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the problem of identifying new, complicated threats and the appropriate countermeasures. In particular, zero-day attacks and multi-step assaults, which...
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| Published in | Journal of cloud computing : advances, systems and applications Vol. 12; no. 1; pp. 14 - 22 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2023
Springer Nature B.V SpringerOpen |
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| Online Access | Get full text |
| ISSN | 2192-113X 2192-113X |
| DOI | 10.1186/s13677-022-00356-9 |
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| Abstract | There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the problem of identifying new, complicated threats and the appropriate countermeasures. In particular, zero-day attacks and multi-step assaults, which are made up of a number of different phases, some malicious and others benign, illustrate this problem well. In this paper, we propose a highly Boosted Neural Network to detect the multi-stageattack scenario. This paper demonstrated the results of executing various machine learning algorithms and proposed an enormously boosted neural network. The accuracy level achieved in the prediction of multi-stage cyber attacks is 94.09% (Quest Model), 97.29% (Bayesian Network), and 99.09% (Neural Network). The evaluation results of the Multi-Step Cyber-Attack Dataset (MSCAD) show that the proposed Extremely Boosted Neural Network can predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction plays a vital role in managing cyber attacks in real-time communication. |
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| AbstractList | There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the problem of identifying new, complicated threats and the appropriate countermeasures. In particular, zero-day attacks and multi-step assaults, which are made up of a number of different phases, some malicious and others benign, illustrate this problem well. In this paper, we propose a highly Boosted Neural Network to detect the multi-stageattack scenario. This paper demonstrated the results of executing various machine learning algorithms and proposed an enormously boosted neural network. The accuracy level achieved in the prediction of multi-stage cyber attacks is 94.09% (Quest Model), 97.29% (Bayesian Network), and 99.09% (Neural Network). The evaluation results of the Multi-Step Cyber-Attack Dataset (MSCAD) show that the proposed Extremely Boosted Neural Network can predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction plays a vital role in managing cyber attacks in real-time communication. Abstract There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the problem of identifying new, complicated threats and the appropriate countermeasures. In particular, zero-day attacks and multi-step assaults, which are made up of a number of different phases, some malicious and others benign, illustrate this problem well. In this paper, we propose a highly Boosted Neural Network to detect the multi-stageattack scenario. This paper demonstrated the results of executing various machine learning algorithms and proposed an enormously boosted neural network. The accuracy level achieved in the prediction of multi-stage cyber attacks is 94.09% (Quest Model), 97.29% (Bayesian Network), and 99.09% (Neural Network). The evaluation results of the Multi-Step Cyber-Attack Dataset (MSCAD) show that the proposed Extremely Boosted Neural Network can predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction plays a vital role in managing cyber attacks in real-time communication. |
| ArticleNumber | 14 |
| Author | Mohammed alsekait, Deema Seth, Bijeta Manoharan, Poongodi Lilhore, Umesh Kumar Raahemifar, Kaamran Dalal, Surjeet Simaiya, Sarita Hamdi, Mounir |
| Author_xml | – sequence: 1 givenname: Surjeet surname: Dalal fullname: Dalal, Surjeet organization: Department of Computer Science and Engineering, Amity University Haryana – sequence: 2 givenname: Poongodi surname: Manoharan fullname: Manoharan, Poongodi email: dr.m.poongodi@gmail.com organization: Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University – sequence: 3 givenname: Umesh Kumar surname: Lilhore fullname: Lilhore, Umesh Kumar organization: School of Computing, University of Louisiana, Department of Computer Science and Engineering, Chandigarh University, Mohali – sequence: 4 givenname: Bijeta surname: Seth fullname: Seth, Bijeta organization: Department of Computer Science and Engineering, B. M. Institute of Engineering & Technology – sequence: 5 givenname: Deema surname: Mohammed alsekait fullname: Mohammed alsekait, Deema organization: Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University – sequence: 6 givenname: Sarita surname: Simaiya fullname: Simaiya, Sarita organization: School of Computing, University of Louisiana, Department of Computer Science and Engineering, Chandigarh University, Mohali – sequence: 7 givenname: Mounir surname: Hamdi fullname: Hamdi, Mounir organization: Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University – sequence: 8 givenname: Kaamran surname: Raahemifar fullname: Raahemifar, Kaamran organization: College of Information Sciences and Technology, Data Science and Artificial Intelligence Program, State College, School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University, Faculty of Engineering, University of Waterloo |
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| Keywords | Multi-stage cyber attack Intrusion detection Zero-day attack Neural network Bayesian network Quest Security Investigation |
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| Snippet | There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the... Abstract There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with... |
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| SubjectTerms | Accuracy Algorithms Bayesian analysis Bayesian network Cloud computing Computer Communication Networks Computer Science Computer System Implementation Computer Systems Organization and Communication Networks Cybersecurity Information Systems Applications (incl.Internet) Intrusion detection Machine learning Multi-stage cyber attack Neural network Neural networks Quest Recent Advancements in Cloud Security using Performance Technologies and Techniques Software Engineering/Programming and Operating Systems Special Purpose and Application-Based Systems Zero-day attack |
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| Title | Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment |
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