From IoT Vulnerabilities to Intrusion Detection: An Explainable Vulnerability-Aware Machine Learning Framework for Smart Home IoT Security

Abstract

The swift deployment of IoT-based smart home appliances has increased the attack surface for the smart environment and exposed it to attacks like botnet command-and-control communications, brute force attacks, denial-of-service attacks, and web-based attacks. Even though the Intrusion Detection Systems (IDSs) that use Machine Learning (ML) algorithms achieve a very high detection rate, most existing solutions focus on predictive performance but lack the ability to link detected attacks to the corresponding vulnerabilities in the Internet of Things (IoT). In this paper, an interpretable vulnerability-aware ML-based approach is presented to solve this problem through the integration of vulnerability classes of IoT, attack classes, network flow attributes, and ML features into one interpretation model. The proposed method uses leakage-aware pre-processing, addressing class imbalance, and compares Random Forest, XGBoost, and soft voting ensemble ML techniques using the CSE-CIC-IDS2018 dataset. Experimental outcomes indicate that XGBoost outperforms the other approaches in terms of performance, with an accuracy of 98.22%, precision of 99.69%, F1-score of 95.36%, ROC-AUC of 99.07%, and only 760 false alarms, which is approximately 19× lower number of false positives compared to Random Forest while keeping a similar level of detection efficiency. In addition to numeric assessment of the approach performance, the suggested model provides the vulnerability-oriented interpretation module that establishes mapping between prominent network flow attributes and possible IoT vulnerability states and attacks. Therefore, the integration of an interpretable vulnerability reasoning component into a high-performing tree-based machine learning algorithm proves to be effective for smart home IoT intrusion detection.