Sustainable Learning-Based Intrusion Detection System for VANETs

Vehicular intrusion detection systems (VIDSs) play a crucial role in protecting the security of vehicular ad hoc networks (VANETs). Recently, numerous researchers have proposed effective vehicular intrusion detection systems to protect the security of VANETs. However, some existing vehicle intrusion systems are susceptible to catastrophic forgetfulness in the process of implementing incremental updates to target novel attacks. Other solutions lack consideration for the continuous updating capabilities of vehicle intrusion detection systems. It is worth mentioning that in the real world, the network attacks suffered by vehicles are not constant, and fixed intrusion detection systems may struggle to detect new network attacks effectively. To address these challenges, we propose an incremental learning-based vehicular intrusion detection scheme that supports continuous updating of the intrusion detection system. Specifically, we design a sample gradient optimization algorithm to enhance the data quality of training samples. Additionally, we utilize locally stored historical data to balance the number of old attack classes for model distillation, thus mitigating the problem of forgetting the old classes as the model learns new classes. The comprehensive experimental results on the CICIDS2017, TON_IOT, and Veremi datasets demonstrate that the proposed vehicular intrusion detection system maintains superior detection accuracy during continuous updating and surpasses the state-of-the-art solution.