A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data

Among the various types of cyberattacks, identifying zero-day attacks is problematic because they are unknown to security systems as their pattern and characteristics do not match known blacklisted attacks. There are many Machine Learning ML models designed to analyze and detect network attacks,...

Flow-Based Detection and Identification of Zero-Day IoT Cameras

The majority of consumer IoT devices lack mechanisms for administrators to monitor and control them, hindering tailored security policies. A key challenge is identifying whether a new device, especially a streaming IoT camera, has joined the network. We present zCamInspector, a system for...