Since its inception, Bitcoin has been subject to numerous thefts due to its enormous economic value. Hackers steal Bitcoin wallet keys to transfer Bitcoin from compromised users, causing huge economic losses to victims. To address the security threat of Bitcoin theft, supervised learning methods were used in this study to detect and provide warnings about Bitcoin theft events. To overcome the shortcomings of the existing work, more comprehensive features of Bitcoin transaction data were extracted, the unbalanced dataset was equalized, and five supervised methods—the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) techniques—as well as three unsupervised methods—the local outlier factor (LOF), one-class support vector machine (OCSVM), and Mahalanobis distance-based approach (MDB)—were used for detection. The best performer among these algorithms was the RF algorithm, which achieved recall, precision, and F1 values of 95.9%. The experimental results showed that the designed features are more effective than the currently used ones. The results of the supervised methods were significantly better than those of the unsupervised methods, and the results of the supervised methods could be further improved after equalizing the training set.

中文翻译：

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基于监督机器学习算法的比特币盗窃检测

自成立以来，比特币由于其巨大的经济价值而遭受了许多盗窃。黑客窃取了比特币钱包密钥，从受感染的用户那里转移了比特币，给受害者造成了巨大的经济损失。为了解决比特币盗窃的安全威胁，本研究中使用了监督学习方法来检测并提供有关比特币盗窃事件的警告。为了克服现有工作的缺点，提取了比特币交易数据的更全面的功能，均衡了不平衡的数据集，并采用了五种监督方法：k最近邻（KNN），支持向量机（SVM），随机森林（RF） ），自适应增强（AdaBoost）和多层感知器（MLP）技术以及三种无监督方法-局部离群因子（LOF），一类支持向量机（OCSVM），和Mahalanobis基于距离的方法（MDB）—用于检测。在这些算法中，性能最好的是RF算法，该算法实现了查全率，精确度和F 1值为95.9％。实验结果表明，所设计的功能比当前使用的功能更有效。监督方法的结果明显优于非监督方法，并且在均衡训练集后可以进一步改善监督方法的结果。