A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
AI-powered edge computing security is moving Intelligent Transportation Systems (ITS) from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT, and advanced 5G for vehicle-to-everything (V2X) connectivity, AI at the edge computing nodes plays a crucial role in protecting against sophisticated threats, enabling URLLC (ultra-low-latency communications) for smart transport, and enhancing infrastructure capabilities and safety. This research applies edge computing to improve latency, bandwidth efficiency, and service responsiveness by movin...
| Authors | Zawad Yalmie Sazid, Robert Abbas, Sasa Maric |
|---|---|
| Primary category | cs.CR |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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