Transaction-Based Hidden Strategies against General Phishing Detection Framework on Ethereum

With the prosperous development of blockchain technologies in the past few years, some cybercrimes have emerged in the blockchain ecosystem, such as the phishing scams on Ethereum. To alleviate these security problems, a few anomaly detection frameworks were proposed. Specifically, previous studies usually model the transfer relationship between accounts in the blockchain ecosystem as a transaction network, where nodes represent accounts and edges represent the corresponding transaction records. Inspired by the adversarial attacks on graph data, we believe the robustness of existing detection frameworks still needs to be further verified even though they have achieved good performance. In this paper, a phishing detection framework based on feature learning and a phishing hidden framework based on inserting transaction records are proposed, respectively. Experimental results show the effectiveness of our phishing detection framework and the superiority of the phishing hidden strategies, which indicate that existing phishing detection frameworks are lack of robustness and still need further improvement against malicious attacks.