计算机科学
利用
入侵检测系统
工业控制系统
人工智能
延迟(音频)
机器学习
深层神经网络
模式(遗传算法)
人工神经网络
低延迟(资本市场)
深度学习
分类器(UML)
互联网
数据挖掘
计算机安全
计算机网络
控制(管理)
电信
万维网
作者
Poulmanogo Illy,Georges Kaddoum,Paulo Freitas de Araujo-Filho,Kuljeet Kaur,Sahil Garg
出处
期刊:IEEE Transactions on Network and Service Management
[Institute of Electrical and Electronics Engineers]
日期:2022-12-01
卷期号:19 (4): 4273-4283
被引量:3
标识
DOI:10.1109/tnsm.2022.3202801
摘要
New industrial control systems (ICSs) that have been modernized with the industrial Internet of Things (IIoT) are exposed to cyber-attacks that exploit IIoT vulnerabilities. Numerous intrusion detection systems (IDSs) have therefore been proposed to secure ICSs, many of which are based on machine learning, specifically deep neural networks (DNNs). Most of the proposed DNN-based solutions rely on single deep learning models and could be less costly in terms of ICS latency. However, they might have difficulties understanding the increasingly complex data distribution of intrusion patterns. Moreover, single deep learning models may not be effective in capturing the specific patterns of minority classes in highly imbalanced datasets, which is usually the case in cyber-security. Therefore, this paper proposes a novel hybrid multistage DNN-based intrusion detection and prevention system (IDPS) with better accuracy for critical ICSs that cannot afford to compromise on security to improve latency. The proposed approach sequentially learns the decision boundaries of the data that were misclassified or classified with low confidence by previous DNNs. Moreover, it incorporates a collaborative intrusion prevention system (IPS) with an emergency response schema that automatically mitigates attacks as soon as anomalies are detected. The results of experimental evaluations performed on different datasets demonstrate the effectiveness of the proposed solution.
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