【Author】
Wen, Haixian; Fang, Junyuan; Wu, Jiajing; Zheng, Zibin
【Source】IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
【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.
【Keywords】Phishing; Blockchains; Feature extraction; Robustness; Perturbation methods; Computer crime; Biological system modeling; Adversarial attacks; blockchain; Ethereum; phishing detection; phishing hiding
【文献类型】Article; Early Access
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