基于权益证明共识机制的用户攻击行为检测

User Attack Behavior Detection Based on PoS Consensus

摘要: 随着区块链技术的快速发展，其去中心化、不可篡改和可追溯等特性在金融、供应链管理和物联网等领域得到了广泛应用。然而，区块链生态系统的开放性和匿名性也为攻击者提供了可乘之机，导致欺诈交易、恶意套利、跨链洗钱等安全问题频发，严重威胁区块链网络的稳定性和用户资产安全。现有的检测方法在应对复杂的交易模式和时序特征等方面仍存在检测精度不足、模型泛化能力有限、实时性较差等问题。因此，研究高效、精准的用户攻击行为检测方法，对提升区块链系统的安全性、增强交易可信度具有重要的现实意义。本文以以太坊平台为研究对象，结合其共识机制特点，联合卷积神经网络和双向长短期记忆网络，构建检测模型。通过利用卷积神经网络提取交易数据的局部特征，双向长短期记忆网络捕捉交易中用户行为的时间依赖关系，并引入注意力机制强化关键特征的权重分配。实验结果表明，该模型在以太坊交易网络检测中达到了很好的效果，实现了对复杂交易模式和异常行为的精准识别。

Abstract: With the rapid development of blockchain technology, its decentralized, tamper resistant, and traceable characteristics have been widely adopted in fields such as finance, supply chain management, and the Internet of Things. However, the openness and anonymity of blockchain ecosystems also create opportunities for attackers, leading to frequent security issues such as fraudulent transactions, malicious arbitrage, and cross-chain money laundering, which severely threaten the stability of blockchain networks and user asset security. Existing detection methods still face challenges including insufficient detection accuracy, limited model generalization capabilities, and poor real-time performance when addressing complex transaction patterns and temporal features. Therefore, researching efficient and precise methods for detecting user attack behaviors holds significant practical importance for enhancing blockchain system security and transaction credibility. In this paper, we take the Ethereum platform as the research object, combine the characteristics of its consensus mechanism, unite the convolutional neural network and bidirectional long and short-term memory network to construct the detection model. By using convolutional neural network to extract the local features of transaction data, bidirectional long and short-term memory network captures the time-dependence of user behaviors in transactions, and introduces the attention mechanism to strengthen the weight allocation of key features. Experimental results show that the model achieves good results in Ethereum transaction network detection, realizing accurate identification of complex transaction patterns and abnormal behaviors.