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Dissecting Payload-based Transaction Phishing on Ethereum

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arxiv 2409.02386 v2 pith:NB6444JW submitted 2024-09-04 cs.CR cs.SE

classification cs.CRcs.SE
keywords phishingptxphishethereumdatasettransactiontransactionscommunitycontributions
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

In recent years, a more advanced form of phishing has arisen on Ethereum, surpassing early-stage, simple transaction phishing. This new form, which we refer to as payload-based transaction phishing (PTXPHISH), manipulates smart contract interactions through the execution of malicious payloads to deceive users. PTXPHISH has rapidly emerged as a significant threat, leading to incidents that caused losses exceeding \$70 million in 2023 reports. Despite its substantial impact, no previous studies have systematically explored PTXPHISH In this paper, we present the first comprehensive study of the PTXPHISH on Ethereum. Firstly, we conduct a long-term data collection and put considerable effort into establishing the first ground-truth PTXPHISH dataset, consisting of 5,000 phishing transactions. Based on the dataset, we dissect PTXPHISH, categorizing phishing tactics into four primary categories and eleven sub-categories. Secondly, we propose a rule-based multi-dimensional detection approach to identify PTXPHISH, achieving over 99% accuracy in the ground-truth dataset. Finally, we conducted a large-scale detection spanning 300 days and discovered a total of 130,637 phishing transactions on Ethereum, resulting in losses exceeding $341.9 million. Our in-depth analysis of these phishing transactions yielded valuable and insightful findings. Furthermore, our work has made significant contributions to mitigating real-world threats. We have reported 1,726 phishing addresses to the community, accounting for 42.7% of total community contributions during the same period. Additionally, we have sent 2,539 on-chain alert messages, assisting 1,980 victims. This research serves as a valuable reference in combating the emerging PTXPHISH and safeguarding users' assets.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KGBERT4Eth: A Feature-Complete Transformer Powered by Knowledge Graph for Multi-Task Ethereum Fraud Detection

    cs.CR 2025-09 conditional novelty 5.0 of 10

    A joint language-model and knowledge-graph pre-training method reports large F1 improvements for Ethereum phishing detection and de-anonymization, but protocol ambiguities remain.

  2. DiT-SGCR: Directed Temporal Structural Representation with Global-Cluster Awareness for Ethereum Malicious Account Detection

    cs.CE 2025-06 reject novelty 5.0 of 10

    DiT-SGCR claims to improve Ethereum malicious account detection by 3.62% to 10.83% F1 over state-of-the-art via directed temporal clustering embeddings, but the supporting experiments and algorithm formulation have cr...

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