Adversarial Attacks on Leakage Detectors in Water Distribution Networks

Paul Stahlhofen, André Artelt, L. Hermes, Barbara Hammer
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引用次数: 1

Abstract

Many Machine Learning models are vulnerable to adversarial attacks: There exist methodologies that add a small (imperceptible) perturbation to an input such that the model comes up with a wrong prediction. Better understanding of such attacks is crucial in particular for models used in security-critical domains, such as monitoring of water distribution networks, in order to devise counter-measures enhancing model robustness and trustworthiness. We propose a taxonomy for adversarial attacks against machine learning based leakage detectors in water distribution networks. Following up on this, we focus on a particular type of attack: an adversary searching the least sensitive point, that is, the location in the water network where the largest possible undetected leak could occur. Based on a mathematical formalization of the least sensitive point problem, we use three different algorithmic approaches to find a solution. Results are evaluated on two benchmark water distribution networks.
输水管网泄漏检测器的对抗性攻击
许多机器学习模型很容易受到对抗性攻击:有一些方法会在输入中添加一个小的(难以察觉的)扰动,从而使模型得出错误的预测。更好地理解这种攻击是至关重要的,特别是对于在安全关键领域(如供水网络监测)使用的模型,以便设计出增强模型稳健性和可信度的对策。我们提出了一种针对配水网络中基于机器学习的泄漏检测器的对抗性攻击的分类。在此基础上,我们关注一种特殊类型的攻击:攻击者搜索最不敏感的点,即在供水网络中可能发生最大未被发现的泄漏的位置。基于最不敏感点问题的数学形式化,我们使用三种不同的算法方法来寻找解。对两个基准配水管网的结果进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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