Machine Learning-driven Intrusion Detection Techniques in Critical Infrastructures Monitored by Sensor Networks - Otoum, Safa (Université d'Ottawa / University of Ottawa, 2019)
In most of critical infrastructures, Wireless Sensor Networks (WSNs) are deployed due to their low-cost, flexibility and efficiency as well as their wide usage in several infrastructures. Regardless of these advantages, WSNs introduce various security vulnerabilities such as different types of attacks and intruders due to the open nature of sensor nodes and unreliable wireless links. Therefore, the implementation of an efficient Intrusion Detection System (IDS) that achieves an acceptable security level is a stimulating issue that gained vital importance. In this thesis, we investigate the problem of security provisioning in WSNs based critical monitoring infrastructures. We propose a trust based hierarchical model for malicious nodes detection specially for Black-hole attacks. We also present various Machine Learning (ML)-driven IDSs schemes for wirelessly connected sensors that track critical infrastructures. In this thesis, we present an in-depth analysis of the use of machine learning, deep learning, adaptive machine learning, and reinforcement learning solutions to recognize intrusive behaviours in the monitored network. We evaluate the proposed schemes by using KDD'99 as real attacks data-sets in our simulations. To this end, we present the performance metrics for four different IDSs schemes namely the Clustered Hierarchical Hybrid IDS (CHH-IDS), Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS) and Q-learning based IDS (QL-IDS) to detect malicious behaviours in a sensor network. Through simulations, we analyzed all presented schemes in terms of Accuracy Rates (ARs), Detection Rates (DRs), False Negative Rates (FNRs), Precision-recall ratios, F_1 scores and, the area under curves (ROC curves) which are the key performance parameters for all IDSs. To this end, we show that QL-IDS performs with ~ 100% detection and accuracy rates.
Keywords: Wireless Sensor Networks (WSNs); Networks security; Deep Learning; Machine Learning; Reinforcement Learning; Intrusion Detection System