Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing

arXiv:2404.13690v1 Announce Type: cross
Abstract: IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, rendering them unusable in detecting compromised IoT devices. In this paper we develop an effective and efficient framework, named CUMAD, to detect compromised IoT devices. Instead of directly relying on individual anomalous …

abstract alerts anomaly anomaly detection arxiv attacks autoencoders cs.cr cs.lg detection devices false focus however hypothesis improving iot iot security protect rendering security testing them type