Hexiang Huang;Xuan Zhang;Jishu Wang;Chen Gao;Xue Li;Rui Zhu;Qiuying Ma
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引用次数: 0
摘要
近年来,区块链在加密货币中的成功应用吸引了大量关注,但也导致违法犯罪活动迅速增长。网络钓鱼诈骗已成为以太坊中最严重的犯罪类型。现有的一些钓鱼欺诈检测方法存在局限性,如复杂性高、可扩展性差、延迟高。在本文中,我们提出了一种名为 "通过基于图神经网络的增强自我图(PEAE-GNN)检测以太坊上的网络钓鱼 "的新型框架。首先,我们从权威网站获取账户标签和交易记录,并提取以标签账户为中心的自我图。然后,我们提出了一种基于结构特征、交易特征和交互强度的特征增强策略,以增强节点特征,从而学习每个自我图的这些特征。最后,我们提出了一种新的图层表示法,将更新后的节点特征按降序排序,然后取前 n 个节点特征的平均值来获得图层表示法,这样既能保留关键信息,又能减少噪声的引入。大量实验结果表明,PEAE-GNN 在网络钓鱼检测任务中取得了最佳性能。同时,我们的框架具有更低的复杂度、更好的可扩展性和更高的效率,能在早期阶段检测到钓鱼账户。
PEAE-GNN: Phishing Detection on Ethereum via Augmentation Ego-Graph Based on Graph Neural Network
Recent years, the successful application of block-chain in cryptocurrency has attracted a lot of attention, but it has also led to a rapid growth of illegal and criminal activities. Phishing scams have become the most serious type of crime in Ethereum. Some existing methods for phishing scams detection have limitations, such as high complexity, poor scalability, and high latency. In this article, we propose a novel framework named phishing detection on Ethereum via augmentation ego-graph based on graph neural network (PEAE-GNN). First, we obtain account labels and transaction records from authoritative websites and extract ego-graphs centered on labeled accounts. Then we propose a feature augmentation strategy based on structure features, transaction features and interaction intensity to augment the node features, so that these features of each ego-graph can be learned. Finally, we present a new graph-level representation, sorting the updated node features in descending order and then taking the mean value of the top n to obtain the graph representation, which can retain key information and reduce the introduction of noise. Extensive experimental results show that PEAE-GNN achieves the best performance on phishing detection tasks. At the same time, our framework has the advantages of lower complexity, better scalability, and higher efficiency, which detects phishing accounts at early stage.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.