BridgeShield: Enhancing Security for Cross-chain Bridge Applications via Heterogeneous Graph Mining
This addresses security vulnerabilities in blockchain interoperability, which is crucial for protecting high-value cross-chain applications, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of detecting attacks on cross-chain bridges by proposing BridgeShield, a framework using heterogeneous graph attention networks to model cross-chain semantics, achieving an average F1-score of 92.58% and a 24.39% improvement over state-of-the-art baselines.
Cross-chain bridges play a vital role in enabling blockchain interoperability. However, due to the inherent design flaws and the enormous value they hold, they have become prime targets for hacker attacks. Existing detection methods show progress yet remain limited, as they mainly address single-chain behaviors and fail to capture cross-chain semantics. To address this gap, we leverage heterogeneous graph attention networks, which are well-suited for modeling multi-typed entities and relations, to capture the complex execution semantics of cross-chain behaviors. We propose BridgeShield, a detection framework that jointly models the source chain, off-chain coordination, and destination chain within a unified heterogeneous graph representation. BridgeShield incorporates intra-meta-path attention to learn fine-grained dependencies within cross-chain paths and inter-meta-path attention to highlight discriminative cross-chain patterns, thereby enabling precise identification of attack behaviors. Extensive experiments on 51 real-world cross-chain attack events demonstrate that BridgeShield achieves an average F1-score of 92.58%, representing a 24.39% improvement over state-of-the-art baselines. These results validate the effectiveness of BridgeShield as a practical solution for securing cross-chain bridges and enhancing the resilience of multi-chain ecosystems.