Adaptive attention-based graph representation learning to detect phishing accounts on the Ethereum blockchain

Haojie Sun, Zhaowei Liu, Shenqiang Wang, haiyang wang

Abstract:

With Ethereum blockchain advancement, the Ethereum platform gathers numerous users. In this context, traditional phishing appears new fraud methods, resulting in significant losses. Currently, network embedding methods are considered effective solutions in the field of phishing detection. However, investigating existing Ethereum phishing node detection algorithms finds they are not optimal and still face two issues. Firstly, the Ethereum network’s topology is unsatisfactory, with nodes exhibiting a long-tail distribution in their degree. Current technologies typically allow high-degree nodes to acquire high quality embeddings, while low-degree nodes, constrained by limited structure, obtain embeddings of lower quality, significantly impacting the detection accuracy of downstream tasks. Secondly, different features of nodes will suffer losses during the fusion process, resulting in the final learned feature embedding being suboptimal. This paper presents an attention-based graphical learning representation approach (ABGRL) to address these problems. ABGRL extracts different feature information by means of multiple channels, and fuses the different feature information using adaptive attention convolution to select the feature information that has the greatest impact on the downstream task. Then the tail node feature information is enhanced by a selfsupervised regression model with robust tail node embedding. Finally, the effectiveness of the proposed model was validated
through extensive experiments.