Explainable AI-based Intrusion Detection in the Internet of Things

Siganos, Marios; Radoglou-Grammatikis, Panagiotis; Kotsiuba, Igor; Markakis, Evangelos; Moscholios, Ioannis; Goudos, Sotirios; Sarigiannidis, Panagiotis

Abstract

The revolution of Artificial Intelligence (AI) has brought about a significant evolution in the landscape of cyberattacks. In particular, with the increasing power and capabilities of AI, cyberattackers can automate tasks, analyze vast amounts of data, and identify vulnerabilities with greater precision. On the other hand, despite the multiple benefits of the Internet of Things (IoT), it raises severe security issues. Therefore, it is evident that the presence of efficient intrusion detection mechanisms is critical. Although Machine Learning (ML) and Deep Learning (DL)-based IDS have already demonstrated their detection efficiency, they still suffer from false alarms and explainability issues that do not allow security administrators to trust them completely compared to conventional signature/specification-based IDS. In light of the aforementioned remarks, in this paper, we introduce an AI-powered IDS with explainability functions for the IoT. The proposed IDS relies on ML and DL methods, while the SHapley Additive exPlanations (SHAP) method is used to explain decision-making. The evaluation results demonstrate the efficiency of the proposed IDS in terms of detection performance and explainable AI (XAI).

Citation

Siganos, M., Radoglou-Grammatikis, P., Kotsiuba, I., Markakis, E., Moscholios, I., Goudos, S., & Sarigiannidis, P. (2023, August). Explainable AI-based Intrusion Detection in the Internet of Things. Presented at ARES 2023: The 18th International Conference on Availability, Reliability and Security, Benevento, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ARES 2023: The 18th International Conference on Availability, Reliability and Security
• Start Date: Aug 29, 2023
• End Date: Sep 1, 2023
• Acceptance Date: Jun 1, 2023
• Online Publication Date: Aug 29, 2023
• Publication Date: 2023-08
• Deposit Date: Nov 10, 2023
• Publicly Available Date: Nov 10, 2023
• Publisher: Association for Computing Machinery (ACM)
• Book Title: ARES '23: Proceedings of the 18th International Conference on Availability, Reliability and Security
• ISBN: 9798400707728
• DOI: https://doi.org/10.1145/3600160.3605162
• Public URL: https://durham-repository.worktribe.com/output/1903950