Facilitating Feature and Topology Lightweighting: An Ethereum Transaction Graph Compression Method for Malicious Account Detection

• URL: http://arxiv.org/abs/2405.08278v3
• Date: Tue, 2 Jul 2024 02:59:02 GMT
• Title: Facilitating Feature and Topology Lightweighting: An Ethereum Transaction Graph Compression Method for Malicious Account Detection
• Authors: Jiajun Zhou, Xuanze Chen, Shengbo Gong, Chenkai Hu, Chengxiang Jin, Shanqing Yu, Qi Xuan,
• Abstract summary: Bitcoin has become one of the primary global platforms for cryptocurrency, playing an important role in promoting the diversification of the financial ecosystem. Previous regulatory methods usually detect malicious accounts through feature engineering or large-scale transaction graph mining. We propose an Transaction Graph Compression method named TGC4Eth, which assists malicious detection by lightweighting both features and topology of the transaction graph.
• Score: 3.877894934465948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ethereum has become one of the primary global platforms for cryptocurrency, playing an important role in promoting the diversification of the financial ecosystem. However, the relative lag in regulation has led to a proliferation of malicious activities in Ethereum, posing a serious threat to fund security. Existing regulatory methods usually detect malicious accounts through feature engineering or large-scale transaction graph mining. However, due to the immense scale of transaction data and malicious attacks, these methods suffer from inefficiency and low robustness during data processing and anomaly detection. In this regard, we propose an Ethereum Transaction Graph Compression method named TGC4Eth, which assists malicious account detection by lightweighting both features and topology of the transaction graph. At the feature level, we select transaction features based on their low importance to improve the robustness of the subsequent detection models against feature evasion attacks; at the topology level, we employ focusing and coarsening processes to compress the structure of the transaction graph, thereby improving both data processing and inference efficiency of detection models. Extensive experiments demonstrate that TGC4Eth significantly improves the computational efficiency of existing detection models while preserving the connectivity of the transaction graph. Furthermore, TGC4Eth enables existing detection models to maintain stable performance and exhibit high robustness against feature evasion attacks.

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