arXiv Machine Learning

Attack Detection using Time Series Foundation Models

arXiv:2606. 06347v1 Announce Type: cross Abstract: This paper addresses the problem of attack detection in cyber-physical systems without any knowledge of the plant model or its structure.

arXiv AI
Jun 3

AI Model Extraction Attacks: Bypassing Single-Client Assumptions in Defenses

arXiv:2606. 03381v1 Announce Type: cross Abstract: Ensuring the protection of Artificial Intelligence (AI) models deployed in military Command and Control (C2) systems and critical infrastructure is essential for maintaining information superiority.

By Maxime Schwarzer, Johannes F. Loevenich, Gustavo S\'anchez, Laurin Holz, Thies M\"ohlenhof, Tobias H\"urten, Roberto Rigolin F. Lopes, Veit Hagenmeyer
arXiv Machine Learning
23h ago

Digital Twin-Based Intrusion Detection for Vehicle Powertrain CAN Bus Systems

arXiv:2608. 17093v1 Announce Type: cross Abstract: Existing automotive intrusion detection systems (IDSs) for the Controller Area Network (CAN) largely target discrepancies in message timing, frequency, or sequencing and cannot detect attacks that preserve these properties while manipulating the payload.

By Araf Rahman, M Sabbir Salek, Mashrur Chowdhury
arXiv Machine Learning
Jun 9

Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks

arXiv:2606. 08473v1 Announce Type: new Abstract: False data injection attacks (FDIAs) introducing small measurement perturbations can still cause large deviations in power system state estimation when the injected signals align with the pseudo-null space of the system model.

By Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar, Yang Weng, Rami Puzis