TY - JOUR
T1 - Hide and Seek
T2 - An Adversarial Hiding Approach Against Phishing Detection on Ethereum
AU - Wen, Haixian
AU - Fang, Junyuan
AU - Wu, Jiajing
AU - Zheng, Zibin
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Adversarial attacks
KW - Biological system modeling
KW - blockchain
KW - Blockchains
KW - Computer crime
KW - Ethereum
KW - Feature extraction
KW - Perturbation methods
KW - Phishing
KW - phishing detection
KW - phishing hiding
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=85139383638&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85139383638&origin=recordpage
U2 - 10.1109/TCSS.2022.3203081
DO - 10.1109/TCSS.2022.3203081
M3 - RGC 21 - Publication in refereed journal
SN - 2329-924X
VL - 10
SP - 3512
EP - 3523
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 6
ER -