针对电力系统中基于自动编码器的网络攻击检测系统的规避性攻击

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-06-04 DOI:10.1016/j.egyai.2024.100381
Yew Meng Khaw , Amir Abiri Jahromi , Mohammadreza F.M. Arani , Deepa Kundur
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引用次数: 0

摘要

电力系统向智能电网的数字化转型过程提高了可靠性、效率和态势感知能力,但同时也增加了网络安全漏洞。鉴于大量智能电网数据的可用性,基于机器学习的方法被认为是改善网络安全态势的有效途径。尽管机器学习方法在增强网络安全方面的优点毋庸置疑,但它们代表了网络攻击面的一个组成部分,特别容易受到敌对攻击。在本文中,我们研究了智能电网中基于自动编码器的网络攻击检测系统对恶意攻击的鲁棒性。首先提出了一种基于迭代的新方法来制作对抗性攻击样本。然后,研究证明,攻击者只要有白盒访问基于自动编码器的网络攻击检测系统的权限,就能利用所提出的方法成功制作出逃避攻击的样本。结果表明,天真的初始对抗性种子无法成功制作对抗性攻击样本,从而揭示了针对智能电网中基于自动编码器的网络攻击检测系统设计对抗性攻击的复杂性。
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Evasive attacks against autoencoder-based cyberattack detection systems in power systems

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.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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