Knowledge Transfer for Collaborative Misbehavior Detection in Untrusted Vehicular Environments
Por:
Sedar R., Kalalas C., Dini P., Vazquez-Gallego F., Alonso-Zarate J., Alonso L.
Publicada:
1 ene 2025
Resumen:
Vehicular mobility underscores the need for collaborative misbehavior detection at the vehicular edge. However, locally trained misbehavior detection models are susceptible to adversarial attacks that aim to deliberately influence learning outcomes. In this paper, we introduce a deep reinforcement learning-based approach that employs transfer learning for collaborative misbehavior detection among roadside units (RSUs). In the presence of label-flipping and policy induction attacks, we perform selective knowledge transfer from trustworthy source RSUs to foster relevant expertise in misbehavior detection and avoid negative knowledge sharing from adversary-influenced RSUs. The performance of our proposed scheme is demonstrated with evaluations over a diverse set of misbehavior detection scenarios using an open-source dataset. Experimental results show that our approach significantly reduces the training time at the target RSU and achieves superior detection performance compared to the baseline scheme with tabula rasa learning. Enhanced robustness and generalizability can also be attained, by effectively detecting previously unseen and partially observable misbehavior attacks.
Filiaciones:
Sedar R.:
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Sustainable AI Research Unit, Castelldefels, 08860, Spain
Kalalas C.:
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Sustainable AI Research Unit, Castelldefels, 08860, Spain
Dini P.:
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Sustainable AI Research Unit, Castelldefels, 08860, Spain
Vazquez-Gallego F.:
i2CAT Foundation, Barcelona, 08034, Spain
Alonso-Zarate J.:
i2CAT Foundation, Barcelona, 08034, Spain
Alonso L.:
Universitat Politècnica de Catalunya (UPC), Signal Theory and Communications Department, Barcelona, 08034, Spain
Green Submitted, All Open Access; Green Accepted Open Access; Green Open Access
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