TGAT: Temporal Graph Attention Network for Blockchain Phishing Scams Detection

Abstract

In recent years, blockchain has emerged as a promising technology with extensive applications in various fields. One of its most notable applications is cryptocurrency. However, the prevalence of phishing scams in blockchain transaction networks has led to significant economic losses and poses a severe threat to transaction security within the cryptocurrency ecosystem. Existing methods for phishing scams detection often employ traditional machine learning techniques or graph embedding methods to extract key information that distinguishes phishing addresses. Nevertheless, these methods often overlook the temporal information within transaction networks, failing to fully capture the dynamic nature of the blockchain transaction network, resulting in suboptimal detection performance. In this paper, we propose a Temporal Graph Attention Network for blockchain phishing scams detection. Specifically, we use a Long Short-Term Memory (LSTM) network to obtain temporal transaction representations. Additionally, we utilize an attention mechanism to aggregate transaction features and features between neighboring nodes. Finally, by incorporating the obtained node representations and the topological characteristics of nodes, we identify phishing addresses using a Multilayer Perceptron (MLP). Experimental results on three real-world Ethereum phishing scams detection datasets indicate that our proposed method significantly outperforms competing approaches.