Machine Learning-Based Transactions Anomaly Prediction for Enhanced IoT Blockchain Network Security and Performance

• Nor Fadzilah Abdullah,
• Ammar Riadh Kairaldeen,
• Asma Abu-Samah,
• Rosdiadee Nordin,

Vol. 18, No. 7, July 31, 2024
10.3837/tiis.2024.07.014, Download Paper (Free):

• Anomaly prediction
• Blockchain
• Clustering Algorithms
• Fraud Detection
• Internet of Things (IoT)
• Legitimate transactions
• Machine Learning
• Privacy
• security.

Abstract

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.

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