The Role of Learning in Attacking Intrusion Detection Systems

Recent work on network attacks have demonstrated that ML-based network intrusion detection systems NIDS can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. ...

A Kolmogorov-Arnold Network for Interpretable Cyberattack Detection in AGC Systems

Automatic Generation Control AGC is essential for power grid stability but remains vulnerable to stealthy cyberattacks, such as False Data Injection Attacks FDIAs, which can disturb the system's stability while evading traditional detection methods. Unlike previous works that relied on blackbox...