A Consortium Chain Anomaly Detection System and Its Detection Method Based on Machine Learning

Abstract

The invention discloses a machine learning-based consortium chain anomaly detection system and a detection method thereof. The detection method arranges the detection system in a consortium chain network based on a PBFT consensus algorithm. For the time interval data of the stage, first determine whether there are abnormal nodes in the alliance chain network, and if there are abnormal nodes, then further determine the abnormal nodes, which can quickly and accurately detect whether there are abnormal nodes in the alliance chain network; use two abnormal detections to ensure In the case of determining abnormal nodes in the consortium chain, reduce the resource occupation of the anomaly detection system. The first anomaly detection detects whether there are abnormal nodes in the consortium chain network through the data of a single node, and the second anomaly detection determines whether there is an abnormality in the consortium chain. nodes; under the premise of ensuring the effectiveness and reliability of detection, the system fully considers the problem of resource occupation, and reduces the resource occupation of the detection system.

Description

technical field [0001] The invention relates to blockchain technology, in particular to a machine learning-based alliance chain anomaly detection system and a detection method thereof. Background technique [0002] Blockchain technology has received the attention of many experts and scholars since it was proposed. This technology has broad application prospects in many fields such as contract processing, data exchange, finance, credit reporting, Internet of Things, logistics, and economic settlement. This technology abandons the traditional trusted third party and is known for its decentralization. According to the degree of decentralization, it can be divided into public chain, alliance chain and private chain. Among them, the public chain is completely decentralized, allowing anyone to join or exit at any time, but it is difficult to correct if there is a problem after its operation, and its consensus mechanism has high overhead, high delay, and low throughput, which canno...

AI Technical Summary

Problems solved by technology

This method uses multi-source heterogeneous data when performing anomaly detection, and its data has a relatively large number of dimensions. Although data dimensionality reduction operations are performed during machine learning, the operation is more complicated

Especially when using complex deep learning, the cost of computing resources and time will be high when learning

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