Zhuo Chen (Zhejiang University), Yufeng Hu (Zhejiang University), Bowen He (Zhejiang University), Dong Luo (Zhejiang University), Lei Wu (Zhejiang University), Yajin Zhou (Zhejiang University)

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 an F1-score of over 99% and processing each block in an average of 390 ms. Finally, we conduct a large-scale detection spanning 300 days and discover 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. Scammers consume approximately 13.4 ETH daily, which accounts for 12.5% of the total Ethereum gas, to propagate address poisoning scams. Additionally, our analysis reveals patterns in the cash-out process employed by phishing scammers, and we find that the top five phishing organizations
are responsible for 40.7% of all losses.

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.

View More Papers

On the Robustness of LDP Protocols for Numerical Attributes...

Xiaoguang Li (Xidian University, Purdue University), Zitao Li (Alibaba Group (U.S.) Inc.), Ninghui Li (Purdue University), Wenhai Sun (Purdue University, West Lafayette, USA)

Read More

Mysticeti: Reaching the Latency Limits with Uncertified DAGs

Kushal Babel (Cornell Tech & IC3), Andrey Chursin (Mysten Labs), George Danezis (Mysten Labs & University College London (UCL)), Anastasios Kichidis (Mysten Labs), Lefteris Kokoris-Kogias (Mysten Labs & IST Austria), Arun Koshy (Mysten Labs), Alberto Sonnino (Mysten Labs & University College London (UCL)), Mingwei Tian (Mysten Labs)

Read More

Sheep's Clothing, Wolf's Data: Detecting Server-Induced Client Vulnerabilities in...

Fangming Gu (Institute of Information Engineering, Chinese Academy of Sciences), Qingli Guo (Institute of Information Engineering, Chinese Academy of Sciences), Jie Lu (Institute of Computing Technology, Chinese Academy of Sciences), Qinghe Xie (Institute of Information Engineering, Chinese Academy of Sciences), Beibei Zhao (Institute of Information Engineering, Chinese Academy of Sciences), Kangjie Lu (University of Minnesota),…

Read More

Eclipse Attacks on Monero's Peer-to-Peer Network

Ruisheng Shi (Beijing University of Posts and Telecommunications), Zhiyuan Peng (Beijing University of Posts and Telecommunications), Lina Lan (Beijing University of Posts and Telecommunications), Yulian Ge (Beijing University of Posts and Telecommunications), Peng Liu (Penn State University), Qin Wang (CSIRO Data61), Juan Wang (Wuhan University)

Read More