Bitcoin Theft Detection Based on Supervised Machine Learning Algorithms

Abstract

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.

Advanced Fraud Detection in Blockchain Transactions: An Ensemble Learning and Explainable AI Approach

“…It was found that the most effective results (accuracy: 97%) were achieved using Ada Boost, Support Vector Machine (SVM), and Random Forest (RF) classifiers, outperforming the other seven algorithms examined. In [37], the researchers focused on extracting more comprehensive features from Bitcoin transaction data to improve the detection of fraudulent activities. To address the issue of imbalanced data, measures were taken to equalize the dataset.…”

Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transactions

“…S. Sayadi [42] proposed a solution to detect fraudulent transactions in cryptocurrency and proposed a technique to determine anomalies in electronic transactions of Bitcoin by machine learning, with high accuracy with k-mean and SVM. B. Chen et al [43] proposed machine learning-assisted solutions to detect Bitcoin theft transactions. The performance of five machine learning models (KNN, SVM, RF, Ad-aBoost, and MLP) is evaluated to identify the theft transactions in Bitcoin.…”

A graph-based deep learning approach for illegal transaction detection in Bitcoin.

“…In the research conducted in 2021 on the Elliptic dataset [18], more generalized features were extracted to overcome the shortcomings of the previous studies. Moreover, by using oversampling methods, they tried to deal with the number of samples for each label in the imbalanced dataset.…”