Online Self-Supervised Learning in Machine Learning Intrusion Detection
  for the Internet of Things
Mert Nakıp Erol Gelenbe
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 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. The approach is experimentally evaluated on public datasets and
compared with well-known ML models, showing that this SSID framework is very
useful and advantageous as an accurate and online learning ML-based IDS for IoT
systems.