Hide and Seek: An Adversarial Hiding Approach Against Phishing Detection on Ethereum

Haixian Wen, Junyuan Fang, Jiajing Wu*, Zibin Zheng

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

16 Citations (Scopus)

Abstract

With the wide application and development of blockchain technology, the past years have witnessed the emergence of various cybercrimes, which have caused a huge amount of economic loss. Among them, phishing scams on the blockchain are regarded as a serious threat to the trading security of the blockchain ecosystem. By modeling the transaction data of blockchain as a network, a series of graph-based phishing detection frameworks have been proposed. Enlightened by adversarial attacks of graph data, we propose to verify the robustness of current phishing detection frameworks under intentional attackers aiming to hide phishing behaviors. In this study, we first propose a general phishing detection framework based on feature engineering and then propose a phishing hiding framework combing the greedy selection mechanism with four phishing hiding strategies to measure the robustness of the proposed general detection models. Extensive experiments evaluate the detective performance of the phishing detection model and its robustness against the hiding framework. The experimental results indicate that the detective model based on feature engineering is rather fragile under adversarial attacks. © 2022 IEEE.
Original languageEnglish
Pages (from-to)3512-3523
JournalIEEE Transactions on Computational Social Systems
Volume10
Issue number6
Online published15 Sept 2022
DOIs
Publication statusPublished - Dec 2023

Research Keywords

  • Adversarial attacks
  • Biological system modeling
  • blockchain
  • Blockchains
  • Computer crime
  • Ethereum
  • Feature extraction
  • Perturbation methods
  • Phishing
  • phishing detection
  • phishing hiding
  • Robustness

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