To address the challenges of topological obscurity and extreme label sparsity in large-scale Ethereum transaction networks, a novel self-supervised phishing detection framework named Eth-GBAV is proposed, integrating graph attention, broad learning, and adversarial variational inference. The framework initiates with a biased random walk strategy guided by transaction intensity and temporal dynamics to capture the initial behavioral semantics of nodes. To distill discriminative features from noisy backgrounds, a “Generative-Attention” encoding architecture is constructed, where a graph attention network aggregates weighted structural neighborhoods and a Variational Autoencoder (VAE) characterizes the underlying probability distribution of legitimate transaction patterns. By maximizing the evidence lower bound, anomalous accounts are effectively isolated through reconstruction residuals. Furthermore, the broad learning system is introduced as an efficient analytical decision layer. By mapping VAE-derived latent embeddings and reconstruction errors into an expanded high-dimensional feature space, the framework captures intricate behavioral correlations via mapping and enhancement neurons. Extensive experimental verification on two large-scale datasets demonstrates the superior performance of Eth-GBAV. On the XBlock dataset, it achieves a leading F1-score of 0.9847 and a recall of 0.9839, outperforming the most competitive state-of-the-art model by significant margins. On the Kaggle dataset, the framework maintains high robustness with an accuracy of 0.9592 and an F1-score of 0.9069.
Song et al. (2026) studied this question.