Knowledge Transfer for Collaborative Misbehavior Detection in Untrusted Vehicular Environments

TL;DR

This paper presents a deep reinforcement learning-based transfer learning approach for collaborative misbehavior detection in vehicular networks, enhancing robustness against adversarial attacks and reducing training time.

Contribution

It introduces a novel transfer learning method that selectively shares knowledge among RSUs to improve detection accuracy and robustness against adversarial attacks.

Findings

Significantly reduces training time at target RSUs.

Achieves superior detection performance over baseline methods.

Effectively detects unseen and partially observable misbehavior attacks.

Abstract

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.