Abstract
Although the consortium blockchains commonly adopt consensus algorithms with Byzantine fault tolerance (such as practical Byzantine fault tolerance (PBFT)), its consensus efficiency will be degraded by the existence of malicious nodes or behaviors. However, the existing researches mainly focus on the detection of malicious behaviors for public blockchains, but are rare about consortium blockchains. In this paper, an anomaly detection model based on machine learning (ML) classification algorithm is proposed for consortium blockchains that adopt PBFT. Besides, a two-stage process is proposed to reduce the resource consumption for anomaly detection. The data needed for proposed model only has two dimensions and is convenient to obtain. The results of experiment show that ML is very effective in anomaly detection for consortium blockchains. Specifically, the algorithms with the highest accuracy are convolutional neural networks (CNN), k-nearest neighbor (KNN) and support vector machines (SVM) in turn. However, KNN and SVM are more suitable because resource consumption of both algorithms are one third of CNN, and the accuracy rates are above 0.9 which is 0.9% lower than CNN.
Supported by Guangxi Key Research and Development Program (Guike AB20238026); Guangxi Science and Technology Base and Talent Special Project of China (Guike AD19110042);Guangxi Natura Science Foundation of China (2018GXNSFDA281054,2018GXNSFAA281232); Guangxi Key Experimental Director Fund of Wireless Broadband Communication and Signal Processing (GXKL06160111).
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Huang, D., Chen, B., Li, L., Ding, Y. (2020). Anomaly Detection for Consortium Blockchains Based on Machine Learning Classification Algorithm. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_25
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