Cyber threat intelligence modeling based on heterogeneous graph convolutional network

Cyber Threat Intelligence (CTI), as a collection of threat information, has been widely used in industry to defend against prevalent cyber attacks. CTI is commonly represented as Indicator of Compromise (IOC) for formalizing threat actors. However, current CTI studies pose three major limitations: first, the accuracy of IOC extraction is low; second, isolated IOC hardly depicts the comprehensive landscape of threat events; third, the interdependent relationships among heterogeneous IOCs, which can be leveraged to mine deep security insights, are unexplored. In this paper, we propose a novel CTI framework, HINTI, to model the interdependent relationships among heterogeneous IOCs to quantify their relevance. Specifically, we first propose multi-granular attention based IOC recognition method to boost the accuracy of IOC extraction. We then model the interdependent relationships among IOCs using a newly constructed heterogeneous information network (HIN). To explore intricate security knowledge, we propose a threat intelligence computing framework based on graph convolutional networks for effective knowledge discovery. Experimental results demonstrate that our proposed IOC extraction approach outperforms existing state-of-the-art methods, and HINTI can model and quantify the underlying relationships among heterogeneous IOCs, shedding new light on the evolving threat landscape.