Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan.
Department of Information Technology, Bayero University Kano, Kano 700006, Nigeria.
Sensors (Basel). 2022 Sep 21;22(19):7162. doi: 10.3390/s22197162.
In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms-XGboost and random forest (RF)-used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.
在本文中，我们解决了比特币网络中的欺诈和异常问题。这些是电子银行和在线交易中常见的问题。然而，随着金融领域的发展，欺诈和异常的手段也在不断发展。此外，区块链技术作为集成到金融领域的最安全方法正在被引入。然而，随着这些先进技术的发展，每年的欺诈行为也在不断增加。因此，我们提出了一种基于机器学习和区块链的安全欺诈检测模型。有两种机器学习算法——XGBoost 和随机森林（RF）——用于交易分类。机器学习技术根据欺诈和综合交易模式训练数据集，并预测新的传入交易。区块链技术与机器学习算法集成，以检测比特币网络中的欺诈交易。在提出的模型中，XGBoost 和随机森林（RF）算法用于分类交易并预测交易模式。我们还计算了模型的精度和 AUC 来衡量准确性。还对提出的智能合约进行了安全分析，以展示我们系统的稳健性。此外，还提出了一种攻击者模型，以保护所提出的系统免受攻击和漏洞的影响。
