Online Self-Supervised Learning in Machine Learning Intrusion Detection for the Internet of Things. (arXiv:2306.13030v1 [cs.CR])

This paper proposes a novel Self-Supervised Intrusion Detection (SSID)
framework, which enables a fully online Machine Learning (ML) based Intrusion
Detection System (IDS) that requires no human intervention or prior off-line
learning. The proposed framework analyzes and labels incoming traffic packets
based only on the decisions of the IDS itself using an Auto-Associative Deep
Random Neural Network, and on an online estimate of its statistically measured
trustworthiness. The SSID framework enables IDS to adapt rapidly to
time-varying characteristics of the …

detection framework human ids internet internet of things intrusion intrusion detection intrusion detection system machine machine learning novel packets ssid system things traffic