An Approach to Identifying Key Hackers in Social Networks

doi:10.3969/j.issn.1671-1122.2023.01.007

Abstract

Abstract:

The situation of computer network security is very serious, so the research on the hackers who carry out network attacks and the organizations where the hackers are located becomes more and more important. Social networks have become the main platform for hackers to communicate with each other and an important channel for network security researchers to obtain information because of their characteristics of not being restricted by time and space. In order to analyze the hackers in social networks, this paper proposed a community detection-based method for identifying key hackers in social networks. Firstly, the article imlemented community segmentation of the network in an unsupervised manner through graph convolutional networks. Secondly, through the improved PageRank algorithm, the topic similarity and interaction between users were used to measure the influence of users in the community. Finally, the efficiency of key hackers in network propagation was evaluated through an independent cascade model. Experiments on the Twitter dataset show that the method can effectively identify key hacker users in social networks.

Key words: key hacker node identification, impact metrics, community detection, social network analysis

CLC Number:

TP309

MA Xiangjun, HE Jingsha, WU Tiejun, FAN Dunqiu. An Approach to Identifying Key Hackers in Social Networks[J]. Netinfo Security, 2023, 23(1): 57-65.

Figures/Tables 5

References 16

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