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.

翻译：本文提出了一种新颖的自监督入侵检测（SSID）框架，该框架能够实现完全基于在线机器学习的入侵检测系统（IDS），无需人工干预或预先的离线学习。所提出的框架仅基于IDS自身使用自联想深度随机神经网络的决策，以及对其统计可信度的在线估计，来分析和标记传入的网络流量数据包。SSID框架使IDS能够快速适应网络流量随时间变化的特性，并消除了离线数据收集的需求。该方法避免了数据标注中的人为错误，以及模型训练和数据收集所需的人力与计算成本。通过在公共数据集上进行实验评估，并与知名机器学习模型进行比较，结果表明该SSID框架作为一种精确且可在线学习的基于机器学习的物联网系统IDS，具有极高的实用价值和优势。