DeXGuardian：基于深度图神经网络的非法加密货币钱包检测系统

DeXGuardian is an illicit cryptocurrency wallet detection system that leverages deep graph neural networks (GNN) to analyze transaction relationship networks between wallets, rather than detecting individual wallets in isolation. This approach addresses the limitations of traditional methods and achieves significant performance improvements in identifying illegal crypto activities like fraud, money laundering, and phishing.

Decentralized Finance (DeFi) has grown rapidly but become a hotbed for malicious activities such as rug pulls, money laundering, and phishing. Traditional security measures and machine learning models fail to effectively detect these threats because they use isolated analysis—ignoring the complex关联 networks between wallets. Malicious actors often use interconnected transaction networks to hide their illegal activities, which isolated methods can't capture.

DeXGuardian models wallet detection as a graph node classification task using deep GNNs. Key steps:

1. Graph Construction: Nodes = wallets, edges = transactions (with weights like amount/frequency). Node features include balance, transaction count, creation time; edge features include amount, timestamp, direction.
2. GNN Architecture: Uses message passing to aggregate neighbor info. Multi-layer graph convolution allows learning high-order neighborhood structure, capturing multi-hop relationships, auto-learning connection weights, and providing interpretability via attention mechanisms.

DeXGuardian outperforms traditional methods in three key areas:

1. Higher Accuracy: Identifies wallets with no obvious individual features but strong links to known malicious addresses via关联推理.
2. Earlier Threat Detection: Catches potential threats early through connections to known malicious addresses even when initial behavior is not obvious.
3. Anti-Disguise Capability: Penetrates intermediate 'clean' wallets to trace the real source of funds via multi-hop analysis.

DeXGuardian can be applied in:

1. Exchange Risk Control: Evaluate new users' wallets to identify accounts linked to illegal addresses before fund deposits.
2. Compliance Supervision: Help regulators monitor large fund flows and identify suspicious money laundering networks for AML compliance.
3. Investor Protection: Integrate into DeFi platforms/wallet services to warn users of high-risk transactions and prevent fraud.

Open Source: The project is open-source, providing a complete workflow (data preprocessing, feature engineering, model training/evaluation) for the blockchain security community. Researchers/developers can build on it (try different GNN architectures, add features, develop real-time systems). Future Outlook:

• Dynamic graph learning (analyze network evolution over time).
• Cross-chain analysis (integrate multi-blockchain data).
• Federated learning (privacy-preserving collaborative training).
• Real-time warning systems (instantly mark suspicious addresses).

DeXGuardian demonstrates the great potential of GNNs in solving blockchain security issues. By shifting from isolated wallet analysis to relationship network analysis, it opens a new technical path for crypto security. It is a valuable project for researchers and practitioners in blockchain security, graph ML, and anti-money laundering.