most citedBridgeShield: Risk-Aware Graph Modeling for Cross-Chain Bridge Attack Detection

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR2025

RISKTAGGER: Evidence-Guided LLM Agent for Post-Incident Forensic Analysis of Money Laundering in Web3

Dan Lin, Yanli Ding, Weipeng Zou +5

Cryptocurrency money-laundering forensic analysis after Web3 incidents faces challenges such as fragmented evidence, expanding transaction paths, and cross-chain discontinuity. Exi…

cs.SI2025

Unnoticeable Community Deception via Multi-objective Optimization

Junyuan Fang, Huimin Liu, Yueqi Peng +3

Community detection in graphs is crucial for understanding the organization of nodes into densely connected clusters. While numerous strategies have been developed to identify thes…

cs.CR2025★ 1 cited

BridgeShield: Risk-Aware Graph Modeling for Cross-Chain Bridge Attack Detection

Dan Lin, Shunfeng Lu, Ziyan Liu +7

Cross-chain bridges enable asset and state transfers across heterogeneous blockchains, but their complex cross-domain interactions introduce new attack surfaces that are difficult…

cs.CR2025

SolPhishHunter: Towards Detecting and Understanding Phishing on Solana

Ziwei Li, Zigui Jiang, Ming Fang +5

Solana is a rapidly evolving blockchain platform that has attracted an increasing number of users. However, this growth has also drawn the attention of malicious actors, with some…

cs.LG2025

Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks

Junyuan Fang, Han Yang, Haixian Wen +3

Graph neural networks have been widely utilized to solve graph-related tasks because of their strong learning power in utilizing the local information of neighbors. However, recent…

cs.LG2025

Mitigating the Structural Bias in Graph Adversarial Defenses

Junyuan Fang, Huimin Liu, Han Yang +3

In recent years, graph neural networks (GNNs) have shown great potential in addressing various graph structure-related downstream tasks. However, recent studies have found that cur…