Sybil-TraceGuard: Traceability-enhanced Sybil Guardian for Connected and Autonomous Vehicles Using Dynamic Semi-supervised GNN
Sybil-TraceGuard, a dynamic semi-supervised spatio-temporal GNN, links fragmented Sybil pseudonyms in connected vehicles back to their source attackers.
The paper proposes Sybil-TraceGuard to attribute Sybil attacks in connected and autonomous vehicles, where adversaries exploit pseudonym-switching while forging Basic Safety Messages. It combines incremental stream detection, dynamic topology-aware graph construction, a spatial GAT encoder, and multi-scale spatio-temporal auditing within a Mean-Teacher semi-supervised framework. Across four attack scenarios with unlabeled ratios of 0.70-0.95, it outperforms state-of-the-art baselines despite extreme class imbalance.
- Focuses on attributing Sybil identities to source attackers, not just detection
- Uses dynamic spatio-temporal graphs with GAT multi-head attention
- Semi-supervised Mean-Teacher training handles minimal labels
- Evaluated across four Sybil attack scenarios with high imbalance
- Products
- Sybil-TraceGuard
From the original article
1 min read at arxiv.orgConnected and autonomous vehicles (CAVs) face severe Sybil attacks, where attackers exploit privacy-preserving pseudonym-switching mechanisms to anomaly alternate identities while forging Basic Safety Messages (BSMs). Although existing schemes can flag suspicious behaviors, these temporally fragmented Sybil identities render traditional single-point and sequence-based deep learning methods ineffective.
By Qian Xu · arxiv.org
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