What is it about?
The Internet of Vehicles (IoV) is envisioned to improve road safety, reduce traffic congestion, and minimize pollution. However, the connectedness of IoV entities increases the risk of cyber attacks, which can have serious consequences. To address these issues, we propose a technique that deploys intrusion detection systems on edge devices without sharing sensitive raw data. In addition, we propose an approach that reduces training time on resource-constrained devices.
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Why is it important?
Traditional intrusion detection systems (IDS) transfer large amounts of raw data to central servers, leading to potential privacy concerns. Also, training IDS on resource-constrained IoV devices generally can result in slower training times and poor service quality. Our approach effectively detects anomalous behavior while preserving data privacy and reducing training time, making it a practical solution for the Internet of Vehicles.
Perspectives
While writing this paper, one thing at the back of my mind was the importance of privacy while thinking about security. I hope that people will also get to think more about privacy-preserving techniques, even as we look for better ways to protect our critical infrastructure.
Paul Agbaje
University of Texas at Arlington
Read the Original
This page is a summary of: Privacy-Preserving Intrusion Detection System for Internet of Vehicles using Split Learning, December 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3632366.3632388.
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