Collaborative Intrusion Detection System for Intermittent IoVs Using Federated Learning and Deep Swarm Particle Optimization

Intelligent vehicles have significantly influenced the advancement of Intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoVs) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging individual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. This work presents a Federated Learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. This approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoVs, optimizing deep learning models for efficient feature selection in secured distributed environments. This cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICIoV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance.