|Online Self-Supervised Learning in Machine Learning Intrusion Detection for the Internet of Things
|Nakip M, Gelenbe E
|Auto-Associative Deep RNN, Botnet attacks, Internet of Things, Intrusion Detection, Machine learning, Random Neural Network (RNN), Self-Supervised Learning
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 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 network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection.