RENOWNED: A Real-Time Anomaly Detection and Mitigation Framework in Edge-Enabled IoV

The rapid adoption of smart vehicles and their interconnection through the Internet of Vehicles (IoV) has increased the use of Electronic Control Units (ECUs) in cars. These ECUs, while enabling advanced features, also present a larger target for cyberattacks, which can disrupt critical functions and jeopardize safety. The time-sensitive nature of automotive systems necessitates swift responses, making the protection of ECUs crucial. The imprecise computation (IC) task model can mitigate the risk of task completion failures by generating acceptable approximation results within deadlines when achieving absolute accuracy becomes difficult within fixed deadlines and energy budgets. This paper introduces RENOWNED, a solution that ensures the normal functioning of these Controller area networks (CAN) controlled ECUs even in the face of anomalies. It combines anomaly detection and mitigation through the HEALING module to maintain the desired performance. The anomaly detection module uses Graph Attention Networks (GAT) to identify unusual processor behaviour. If an anomaly is detected, the HEALING module takes over, reallocating tasks based on the available resources to guarantee that deadlines are met and energy constraints are not exceeded. Experiments have shown that RENOWNED delivers a Quality of Service (QoS) of 25% to 64% when system utilisation is varied in the range from 40% to 90%. It exhibits an excelling performance in detecting anomalies, achieving a 97.6% accuracy even when the magnitude mixed anomaly signals are very minute. Thus our proposed RENOWNED offers a robust way to enhance the reliability and energy efficiency of safety-critical automotive applications prevalent in IoV.