Adversarial Attacks on Leakage Detectors in Water Distribution Networks
- URL: http://arxiv.org/abs/2306.06107v1
- Date: Thu, 25 May 2023 12:05:18 GMT
- Title: Adversarial Attacks on Leakage Detectors in Water Distribution Networks
- Authors: Paul Stahlhofen, Andr\'e Artelt, Luca Hermes, Barbara Hammer
- Abstract summary: We propose a taxonomy for adversarial attacks against machine learning based leakage detectors in water distribution networks.
Based on a mathematical formalization of the least sensitive point problem, we use three different algorithmic approaches to find a solution.
- Score: 6.125017875330933
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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.
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