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CAD: Command-Level Anomaly Detection for Vehicle-Road Collaborative Charging Network

A large number of charging piles installed on roadside parking spaces and smart poles on the roadside of the Internet have become essential substation infrastructure (roadside) for building a vehicle-road coordinated charging network for electric vehicles. The management system of China National Grid’s network-load interaction includes the interaction between these main stations (traffic control stations) and substations (roadsides). The Internet-friendly interactive communication protocol for China’s vehicle-road coordination is IEC 60870-5-104 (104 protocol). The control network of the vehicle-road collaborative charging network has many characteristics, such as multiple levels, multiple types, and frequent information exchange for monitoring and control. Various types of operational information and control commands are subject to eavesdropping, tampering, and interruption during collection, transmission, and triggering. This paper proposes a command-level anomaly detection (CAD) method for a vehicle-road collaborative charging network. The CAD method analyzes the protocol for the specification format and business command characteristics of the 104 protocol. This paper uses the dynamic analysis protocol fuzzy test to realize the dynamic information in the program to guide the generation of test cases and pass the Markov state transition diagram. We describe the state transition and abnormality identification of protocol messages. We also design a long-term memory network to implement instruction-level anomaly feature mining. The experiment proved the validity of CAD. If we adopt other new protocols for the vehicle-road coordinated network in different countries or regions, the analysis of the new protocol can be completed in the same way, which has strong application value and prospect.