面向区块链网络的异常检测方法综述

doi:10.3778/j.issn.1002-8331.2408-0086

摘要/Abstract

摘要： 区块链技术具有去中心化、不可篡改等特性，虽然增强了系统的可信性和可靠性，但是也给区块链日益复杂的应用平台带来了诸多的异常问题。为了确保区块链系统的平稳运行，对区块链上恶意攻击、非法活动等异常行为的检测是至关重要的。系统地梳理了区块链应用层、合约层、网络层及共识层面临的安全风险及其异常检测工作，并进一步针对将不同异常问题的检测方法根据特征提取形式进行总结分类，从规则分析、机器学习、表示学习和深度学习的角度进行了分析。最后，对区块链异常检测领域现存的问题提出了未来研究方向。

关键词: 区块链, 异常检测, 机器学习, 深度学习, 规则分析

Abstract: Blockchain technology possesses characteristics such as decentralization and immutability, which enhance the system’s credibility and reliability. However, these features also introduce numerous anomalies to the increasingly complex blockchain application platforms. To ensure the smooth operation of blockchain systems, it is crucial to detect malicious attacks, illegal activities, and other abnormal behaviors on the blockchain. This paper systematically reviews the security risks and anomaly detection efforts at the application layer, contract layer, network layer, and consensus layer of blockchain. Furthermore, it categorizes different anomaly detection methods based on their feature extraction approaches, analyzing them from the perspectives of rule-based analysis, machine learning, representation learning, and deep learning. Finally, it proposes future research directions for addressing the existing issues in the field of blockchain anomaly detection.

Key words: blockchain, abnormal detection, machine learning, deep learning, rule analysis

万季玲, 曹利峰, 白金龙, 李金辉, 杜学绘. 面向区块链网络的异常检测方法综述[J]. 计算机工程与应用, 2025, 61(13): 78-99.

WAN Jiling, CAO Lifeng, BAI Jinlong, LI Jinhui, DU Xuehui. Survey of Anomaly Detection Methods for Blockchain Networks[J]. Computer Engineering and Applications, 2025, 61(13): 78-99.

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