HAVEN: A Hybrid Anomaly Detection System for Intra-Vehicular CAN-Bus Communication Using Rule-Based and Neural Networks

Modern vehicles use ECUs (electronic control units, specialized computers that control vehicle functions) connected through CAN-bus networks (a communication system that lets these computers talk to each other), but this setup is vulnerable to cyberattacks like DOS (denial of service, overwhelming a system with requests) and fuzzing (sending random data to find weaknesses). This paper presents HAVEN, a hybrid anomaly detection system that combines rule-based checks with machine learning (teaching computers to recognize patterns) and neural networks (algorithms inspired by how brains process information) to identify suspicious activity on vehicle networks, achieving high accuracy while running quickly.