Hybrid Edge Intelligence for Real-Time Intrusion Detection in Advanced Traffic Management Systems
Rohith Reddy Depa, Yunpeng Zhang, Dianxiang Xu · 2025
Advanced Traffic Management Systems (ATMS) are critical to urban mobility. Still, they are increasingly vulnerable to cyberattacks due to their reliance on interconnected Internet of Things (IoT) and Vehicle-to-Everything (V2X) infrastructure. Existing intrusion detection systems (IDS) struggle with real-time performance, data scarcity, and resource constraints on edge devices. This paper proposes a hybrid framework that combines rule-based filtering with machine learning powered by transfer learning (TL) optimized for edge deployment. Our framework aims to achieve 95% accuracy and <20ms latency on a Raspberry Pi 4, bridging deterministic rules and adaptive Artificial Intelligence (AI) for secure, explainable, and resource-efficient cybersecurity in ATMS.