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A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems

A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems

Srikanth Yadav M., Kalpana R.
Copyright: © 2021 |Pages: 23
ISBN13: 9781799851011|ISBN10: 179985101X|EISBN13: 9781799851028
DOI: 10.4018/978-1-7998-5101-1.ch007
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MLA

M., Srikanth Yadav, and Kalpana R. "A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems." Artificial Intelligence Paradigms for Smart Cyber-Physical Systems, edited by Ashish Kumar Luhach and Atilla Elçi, IGI Global, 2021, pp. 137-159. https://doi.org/10.4018/978-1-7998-5101-1.ch007

APA

M., S. Y. & R., K. (2021). A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems. In A. Luhach & A. Elçi (Eds.), Artificial Intelligence Paradigms for Smart Cyber-Physical Systems (pp. 137-159). IGI Global. https://doi.org/10.4018/978-1-7998-5101-1.ch007

Chicago

M., Srikanth Yadav, and Kalpana R. "A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems." In Artificial Intelligence Paradigms for Smart Cyber-Physical Systems, edited by Ashish Kumar Luhach and Atilla Elçi, 137-159. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-5101-1.ch007

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Abstract

In the present computing world, network intrusion detection systems are playing a vital part in detecting malicious activities, and enormous attention has been given to deep learning from several years. During the past few years, cyber-physical systems (CPSs) have become ubiquitous in modern critical infrastructure and industrial applications. Safety is therefore a primary concern. Because of the success of deep learning (DL) in several domains, DL-based CPS security applications have been developed in the last few years. However, despite the wide range of efforts to use DL to ensure safety for CPSs. The major challenges in front of the research community are developing an efficient and reliable ID that is capable of handling a large amount of data, in analyzing the changing behavioral patterns of attacks in real-time. The work presented in this manuscript reviews the various deep learning generative methodologies and their performance in detecting anomalies in CPSs. The metrics accuracy, precision, recall, and F1-score are used to measure the performance.

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