This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) 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 {machine learning and deep learning} models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-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，该框架实现了完全基于在线深度学习的入侵检测系统（IDS），无需人工干预或先前的离线学习。所提出的框架仅根据IDS自身的决策，利用自联想深度随机神经网络，并结合其统计可信度的在线估计，对传入流量数据包进行分析和标记。SSID框架使IDS能够快速适应网络流量的时变特性，并消除了离线数据收集的需求。该方法避免了数据标记中的人为错误，以及模型训练和数据收集所需的人力与计算成本。该方法在公开数据集上进行了实验评估，并与知名的机器学习和深度学习模型进行了比较，结果表明，该SSID框架作为面向物联网系统的精确在线深度学习入侵检测系统，具有显著的实用性和优势。