Detecting covert channel attacks on cyber-physical systems

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hongwei Li, Danai Chasaki
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引用次数: 0

Abstract

Cyberattacks on cyber-physical systems (CPS) have the potential to cause widespread disruption and affect the safety of millions of people. Machine learning can be an effective tool for detecting attacks on CPS, including the most stealthy types of attacks, known as covert channel attacks. In this study, the authors describe a novel hierarchical ensemble architecture for detecting covert channel attacks in CPS. Our proposed approach uses a combination of TCP payload entropy and network flows for feature engineering. Our approach achieves high detection performance, shortens the model training duration, and shows promise for effective detection of covert channel communications. This novel architecture closely mirrors the CPS attack stages in real-life, providing flexibility and adaptability in detecting new types of attacks.

Abstract Image

检测对网络物理系统的隐蔽通道攻击
对网络物理系统(CPS)的网络攻击有可能造成大范围的破坏,影响数百万人的安全。机器学习可以成为检测 CPS 攻击的有效工具,其中包括最隐蔽的攻击类型,即隐蔽通道攻击。在这项研究中,作者描述了一种用于检测 CPS 中隐蔽信道攻击的新型分层集合架构。我们提出的方法结合使用 TCP 有效载荷熵和网络流来进行特征工程。我们的方法实现了较高的检测性能,缩短了模型训练时间,并显示出有效检测隐蔽信道通信的前景。这种新颖的架构密切反映了现实生活中的 CPS 攻击阶段,为检测新型攻击提供了灵活性和适应性。
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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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