Adversarial Attacks on Leakage Detectors in Water Distribution Networks
This work addresses security vulnerabilities in critical infrastructure monitoring, but it is incremental as it builds on existing adversarial attack concepts in a specific domain.
The paper tackles the problem of adversarial attacks on machine learning-based leakage detectors in water distribution networks by proposing a taxonomy and focusing on finding the least sensitive point for undetected leaks, with results evaluated on two benchmark networks.
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.