TiGNet: Joint entity and relation triplets extraction for APT campaign threat intelligence

Contemporary cybersecurity faces escalating challenges from sophisticated threats, notably Advanced Persistent Threats (APTs). Addressing these challenges necessitates a collaborative, multidisciplinary approach that transcends traditional boundaries. Gathering cyber threat intelligence (CTI) on APT campaigns and constructing a comprehensive knowledge graph empowers defenders to track the latest trends in these campaigns, update defense strategies, and attain crucial advantages in defense measures. Previous works used relation extraction techniques to obtain entity-relation triplets for constructing threat intelligence knowledge graphs. However, these works either rely on pipeline workflow, are susceptible to exposure errors and error propagation, or use sequence annotation method, which combines entity and relation labels but lacks the ability to extract single entity overlaps (SEO) or subject-object overlaps (SOO) triplets. This paper introduces TiGNet, a novel method that transforms the entity-relation triplet’s extraction task into multiple token-span recognition tasks utilizing token-pair matrices. Additionally, we integrate GlobalPointer to incorporate token position information into the token-pair matrix, significantly enhancing extraction performance. To facilitate method evaluation, we annotated a Chinese entity-relation triplets dataset about APT campaigns, named APT-Triplets, comprising 9711 triplets encompassing seven triplet types. Our evaluation demonstrates that TiGNet improves the F1 score of 3.79-5.59 compared to previous joint extraction methods. Furthermore, it outperforms methods based on large language models (LLMs) in terms of both extraction performance and inference time. These results underscore TiGNet’s capacity to accurately and swiftly extract threat intelligence, facilitating the construction of the APT campaign knowledge graphs, empowering defenders to track evolving trends and fortify defense strategies collaboratively.