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Title	Collaborative Intrusion Detection System For Intermittent 10 Vs Using Federated Learning And Deep Swarm Particle Optimization
ID_Doc	14729
Authors	Ullah F.; Srivastava G.; Mostarda L.; Cacciagrano D.
Year	2024
Published	2024 IEEE 11th International Conference on Data Science and Advanced Analytics, DSAA 2024
DOI	http://dx.doi.org/10.1109/DSAA61799.2024.10722781
Abstract	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 (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual 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 IoV s, 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 CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance. © 2024 IEEE.
Author Keywords	Cybersecurity; Federated Learning; Intelligent Transportation System; Internet of Vehicles; Intrusion Detection

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