A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks

Data integrity attacks in transportation systems can have serious consequences, and centralized detection approaches create single points of failure. This work is innovative because it distributes anomaly detection to network edge while maintaining ability to identify complex, multi-sensor attacks that try to evade detection. The zone-level and sensor-level detection hierarchy enables efficient resource utilization at constrained edge devices.

This paper presents a decentralized anomaly detection framework for smart transportation networks that distributes detection across road side units while identifying orchestrated data integrity attacks. The system employs zone-level detection at RSUs combined with sensor-level detection to handle both deductive and camouflage attacks where adversaries manipulate speed readings. The approach uses hierarchical clustering algorithms for RSU placement optimization.

The RSU clustering approach successfully optimized RSU placement to concentrate resources where sensors were dense while minimizing communication overhead. The system demonstrated ability to detect deductive attacks where individual sensor readings are altered and camouflage attacks where multiple sensors are manipulated to evade detection. The decentralized approach improved computational efficiency while maintaining detection accuracy.