HIN-CTIA CYBER THREAT INTELLIGENCE MODELING AND IDENTIFICATION SYSTEM BASED ON HETEROGENEOUS INFORMATION NETWORK (Phoenix: International Multidisciplinary Research Journal ( Peer reviewed High Impact Journal ), 2023)
There has been an increase in the number of businesses prepared to use cyber threat intelligence (CTI) to better understand the cyber security landscape. Automatically identifying the danger type of infrastructure nodes for early warning is difficult due to the restricted labels of cyber threat infrastructure nodes included in CTI. To overcome these obstacles, we create the practical system HinCTI, which models cyber threat intelligence and classifies different kinds of threats. To illustrate the semantic connection between infrastructure nodes, we first create a threat intelligence meta-schema. We then apply the CTI model to an HIN simulation. Next, we define a threat Infrastructure similarity measure between threat infrastructure nodes based on meta-paths and meta-graph instances, and we introduce a MIIS measure-based heterogeneous graph convolutional network (GCN) approach for determining the types of infrastructure nodes that pose threats to CTI. To the best of our knowledge, this is the first effort to present a heterogeneous GCN-based method to threat type identification of infrastructure nodes and to model CTI on HIN for threat identification. Extensive tests are run on real-world datasets using HinCTI, and the findings show that our suggested methodology can greatly outperform the state-of-the-art baseline approaches in terms of threat type detection.