Evasion Attacks on Cost-Utility-Based Adversarial Training for Online AutoML in IoT Networks

arXiv:2609.31981v1 Announce Type: new Abstract: As Internet of Things (IoT) networks increasingly depend on machine learning for anomaly, malware, intrusion detection, and network monitoring, such systems have become attractive targets for evasion attacks. Evasion attacks pose a major security risk because an adversary intentionally modifies input data to mislead a trained model into producing incorrect predictions while evading detection.

Read the full article at arXiv cs.CR →