aiqbench analysis
BridgeShield introduces a graph-based framework that leverages AI techniques like differential meta-path selection to detect cross-chain bridge attacks by modeling complex interactions across multiple blockchain networks. By capturing structural deviations in execution traces rather than relying on static rules, the system effectively identifies novel attack patterns with high precision. This research advances the intersection of machine learning and cryptographic security, offering a scalable solution for monitoring heterogeneous blockchain ecosystems against evolving threats.
BridgeShield: Risk-Aware Graph Modeling for Cross-Chain Bridge Attack Detection
arXiv:2508.20517v2 Announce Type: replace-cross Abstract: Cross-chain bridges enable asset and state transfers across heterogeneous blockchains, but their complex cross-domain interactions introduce new attack surfaces that are difficult to monitor using traditional single-chain analysis methods. Existing approaches often focus on isolated on-chain behaviors and fail to capture the multi-stage execution semantics of cross-chain transactions. This paper presents BridgeShield, a graph-based framework for detecting cross-chain bridge attacks through risk-aware modeling of cross-chain execution behaviors. BridgeShield reconstructs cross-chain behavior graphs from execution traces and event logs, capturing interactions across the source chain, off-chain relay components, and the destination chain. To highlight attack-relevant structures, the framework employs differential meta-path selection to identify execution patterns that exhibit structural deviations between normal and attack transactions, and hierarchical risk propagation to aggregate distributed risk signals across interaction stages. Experiments on real-world bridge incidents show that BridgeShield achieves an F1-score of 92.6% in cross-chain attack detection and consistently outperforms existing rule-based and graph-based baselines. In addition, the model remains effective in detecting previously unseen attack incidents, indicating that BridgeShield captures structural risk patterns rather than memorizing historical attack templates.
Abstract:Cross-chain bridges enable asset and state transfers across heterogeneous blockchains, but their complex cross-domain interactions introduce new attack surfaces that are difficult to monitor using traditional single-chain analysis methods. Existing approaches often focus on isolated on-chain behaviors and fail to capture the multi-stage execution semantics of cross-chain transactions. This paper presents BridgeShield, a graph-based framework for detecting cross-chain bridge attacks through risk-aware modeling of cross-chain execution behaviors. BridgeShield reconstructs cross-chain behavior graphs from execution traces and event logs, capturing interactions across the source chain, off-chain relay components, and the destination chain. To highlight attack-relevant structures, the framework employs differential meta-path selection to identify execution patterns that exhibit structural deviations between normal and attack transactions, and hierarchical risk propagation to aggregate distributed risk signals across interaction stages. Experiments on real-world bridge incidents show that BridgeShield achieves an F1-score of 92.6% in cross-chain attack detection and consistently outperforms existing rule-based and graph-based baselines. In addition, the model remains effective in detecting previously unseen attack incidents, indicating that BridgeShield captures structural risk patterns rather than memorizing historical attack templates.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2508.20517 [cs.CR]
(or arXiv:2508.20517v2 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2508.20517 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 272 words
