PhishEye uses temporal graph contrastive learning on heterogeneous Ethereum transaction graphs for self-supervised phishing detection, achieving F1 scores of 87.23% for transactions and 94.19% for accounts while identifying 1,803 new phishing addresses in real-world deployment that prevented over 2B
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Phishing Detection in Ethereum via Temporal Graph Contrastive Learning
PhishEye uses temporal graph contrastive learning on heterogeneous Ethereum transaction graphs for self-supervised phishing detection, achieving F1 scores of 87.23% for transactions and 94.19% for accounts while identifying 1,803 new phishing addresses in real-world deployment that prevented over 2B