Evasive attacks against autoencoder-based cyberattack detection systems in power systems  

作　　者：Yew Meng Khaw Amir Abiri Jahromi Mohammadreza F.M.Arani Deepa Kundur 

机构地区：[1]Department of Electrical and Computer Engineering,University of Toronto,Toronto,ON M5S 3G4,Canada [2]School of Electronic and Electrical Engineering,University of Leeds,Leeds,LS29JT,United Kingdom [3]Department of Electrical,Computer&Biomedical Engineering,Toronto Metropolitan University(formerly Ryerson University),Toronto,ON M5B 2K3,Canada

出　　处：《Energy and AI》2024年第3期126-135,共10页能源与人工智能（英文）

摘　　要：The digital transformation process of power systems towards smart grids is resulting in improved reliability, efficiency and situational awareness at the expense of increased cybersecurity vulnerabilities. Given the availability of large volumes of smart grid data, machine learning-based methods are considered an effective way to improve cybersecurity posture. Despite the unquestionable merits of machine learning approaches for cybersecurity enhancement, they represent a component of the cyberattack surface that is vulnerable, in particular, to adversarial attacks. In this paper, we examine the robustness of autoencoder-based cyberattack detection systems in smart grids to adversarial attacks. A novel iterative-based method is first proposed to craft adversarial attack samples. Then, it is demonstrated that an attacker with white-box access to the autoencoder-based cyberattack detection systems can successfully craft evasive samples using the proposed method. The results indicate that naive initial adversarial seeds cannot be employed to craft successful adversarial attacks shedding insight on the complexity of designing adversarial attacks against autoencoder-based cyberattack detection systems in smart grids.

关 键 词：CYBERSECURITY Adversarial attacks Anomaly detection Iterative-based methods Substati on automati on 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]