A Blended Active Detection Strategy for False Data Injection Attacks in Cyber-Physical Systems
A Blended Active Detection Strategy for False Data Injection Attacks in Cyber-Physical Systems
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DOI：
10.1109/tcns.2020.3024315
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发表时间：
2021-03
影响因子：
4.2
通讯作者：
Mohsen Ghaderi;Kian Gheitasi;Walter Lúcia
中科院分区：
文献类型：
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作者：
Mohsen Ghaderi;Kian Gheitasi;Walter Lúcia
近年来，已经提出了不同的解决方案来检测针对网络控制系统的高级隐形网络攻击。在这篇文章中，我们提出了一个混合检测方案，适当地利用和结合现有的检测思想，即水印和运动目标。特别是，水印信号和非线性静态辅助函数相结合，既限制了攻击者的披露资源，并获得一个不可识别的移动目标。该方案能够检测广泛的虚假数据注入攻击，包括零动态，重放和隐蔽攻击。此外，它表明，该方法减轻了标准的移动目标和水印防御策略的缺点。最后，一个广泛的模拟研究报告，以对比所提出的检测器与最近的竞争对手的计划，并提供切实的证据所提出的解决方案的有效性。
In recent years, different solutions have been proposed to detect advanced stealthy cyber-attacks against networked control systems. In this article, we propose a blended detection scheme that properly leverages and combines two existing detection ideas, namely, watermarking and moving target. In particular, a watermarked signal and a nonlinear static auxiliary function are combined to both limit the attacker's disclosure resources and obtain an unidentifiable moving target. The proposed scheme is capable of detecting a broad class of false data injection attacks, including zero-dynamics, replay, and covert attacks. Moreover, it is shown that the proposed approach mitigates the drawbacks of standard moving target and watermarking defense strategies. Finally, an extensive simulation study is reported to contrast the proposed detector with recent competitor schemes and provide tangible evidence of the effectiveness of the proposed solution.