IoT-enabled Adaptive Intrusion Detection Systems for Autonomous Vehicle Cybersecurity

Authors

  • Dr. Gül Büke Öztürk Associate Professor of Electrical and Electronics Engineering, Istanbul Technical University, Turkey Author

Keywords:

Motivation, Active Learning Intrusion Detection Systems

Abstract

Autonomous vehicles (AVs) are the future of the transportation industry. However, their reliance on Internet of Things (IoT) technologies introduces several cybersecurity threats. The ability to extend the Adaptive or the Active Learning Intrusion Detection Systems (AD-IDS/AL-IDS) to be able to explain their detections in real time is a critical missing piece in ensuring the safety and security of AVs. We implement SHapley Additive Explanation (SHAP) in the AL-IDS and the AD-IDS to develop two Explainable AI (XAI) models. We provide the steps, experiences, and lessons learned to simulate real-time detection in AVs with the StarCraft II, an open-source space game with different attack scenarios similar to those encountered by AVs. The real-time detection mechanism implemented provides transparency into decisions made by the IDS, which is a significant contribution regarding the wide application of AI models in domains where safety and trust are critical.

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Published

30-06-2023

How to Cite

[1]
Dr. Gül Büke Öztürk, “IoT-enabled Adaptive Intrusion Detection Systems for Autonomous Vehicle Cybersecurity”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 1–24, Jun. 2023, Accessed: Dec. 25, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/86

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