Traceability method links fake car identities to attackers in autonomous networks
Sybil-TraceGuard: Traceability-enhanced Sybil Guardian for Connected and Autonomous Vehicles Using Dynamic Semi-supervised GNN
Cryptography and Security
Summary
Connected and autonomous vehicles rely on messages to share safety information, but attackers can pretend to be many different cars using fake identities, confusing security systems. The authors created Sybil-TraceGuard, a tool that not only detects these attacks but also traces the fake identities back to the single attacker, even when there are very few labeled examples to learn from. Their method uses a special kind of graph neural network that looks at both space and time patterns among vehicles. Tests show it works better than existing methods, handling high levels of uncertainty and imbalance in the data.
What this means in practice
- •For automotive cybersecurity teams: Identify and trace attackers creating fake vehicle identities in autonomous vehicle communication networks to enhance security response and remediation.
- •For network security engineers: Improve detection systems by linking fragmented fake nodes to single sources within privacy-preserving vehicle networks, reducing false alarms.
Authors
Qian Xu, Jiaxun Zhang, Chengyue Wang, Zhenning Li
Abstract
Connected 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. Linking these fragmented identities back to the source attacker is essential for root-cause elimination, particularly under extreme label scarcity. Therefore, the Sybil-TraceGuard is proposed as a dynamic semi-supervised spatio-temporal GNN framework for Sybil Guardian, prioritizing "who is responsible" over "whether an attack is happening". It comprises four tightly coupled modules: Incremental Stream Attack Detection (ISAD) for efficient Sybil attack pre-screening; the Dynamic Topology-aware Constructor (DTC) for constructing spatio-temporal dynamic graphs; the Spatial GAT-Encoder with Multi-head Attention (SGEM) to capture multi-identity logical conflicts in spatial interactions; and the Multi-scale Spatio-Temporal Audit (MSTA) to audit short-term and long-term temporal inconsistencies. These modules are optimized within a semi-supervised Mean-Teacher framework via feature-edge shuffling perturbations, regularizing the latent feature space using minimal labels. Experiments across four Sybil attack scenarios demonstrate that Sybil-TraceGuard effectively links fragmented pseudonyms to source attackers. It outperforms state-of-the-art baselines across unlabeled ratios of 0.70-0.95, maintaining high stability and sensitivity despite extreme class imbalance and varying hyperparameter settings.