Eth-PSD: A Machine Learning-Based Phishing Scam Detection Approach in Ethereum

网络钓鱼 计算机科学 人工智能 机器学习 计算机安全 操作系统 互联网
作者
Arkan Hammoodi Hasan Kabla,Mohammed Anbar,Selvakumar Manickam,Shankar Karupayah
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:10: 118043-118057 被引量:21
标识
DOI:10.1109/access.2022.3220780
摘要

Recently, the rapid flourish of blockchain technology in the financial field has attracted many cybercriminals' attention to launch blockchain-based attacks such as Ponzi schemes, Scam wallets, and phishing scams. Currently, Ethereum is the most prominent blockchain-based platform and the first that supports smart contracts. However, the number of phishing scam accounts are reportedly more than 50% of all cybercrimes in Ethereum. In contrast, this paper proposes a detection mechanism called Ethereum Phishing Scam Detection (Eth-PSD) that attempts to detect phishing scam-related transactions using a novel machine learning-based approach. Eth-PSD tackles some of the limitations in the existing works, such as the use of imbalanced datasets, complex feature engineering, and lower detection accuracy. We also investigated the aspects of constructing a new updated and balanced dataset that can be used for evaluating Eth-PSD effectively. Our experimental results indicate that Eth-PSD could efficiently detect the phishing scam on Ethereum with a detection accuracy of 98.11%, with a very low False Positive Rate of 0.01. Taken together, Eth-PSD showed a superior advantage compared to the existing works in reducing the dimensionality of the dataset by feature engineering and achieved an overall detection accuracy with an improvement of at least 6% compared to other existing solutions from the related work.
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