Tracing Relevant Twitter Accounts Active in Cyber Threat Intelligence Domain by Exploiting Content and Structure of Twitter Network

Due to the enormous volume of data and rate of data generation on Twitter, a challenging task is to trace user accounts to monitor these as instances of Cyber Threat Intelligence (CTI). In this paper, we propose a novel approach for cyber threat-associated user accounts tracing in the Twitter data stream based on the ranking of users according to their contextual relevance and topological information extracted from finding user communities in the Twitter network. In our approach, we use both structural information of the graph network and user accounts’ tweet contents to find relevant user accounts concerning previously identified seed user accounts. Our proposed method outperforms over two relevant user recommendation methods on an annotated data of CTI related Twitter user accounts in tracing relevant user accounts as instances of cyber-threat intelligence.