CVE-TTP KG: Knowledge Graph Linking Software Vulnerabilities to Attack Behaviors
Authors: Swati Yadav, Dincy R. Arikkat, Basant Agarwal, Serena Nicolazzo, Antonino Nocera, Vinod P
Abstract
In the evolving threat landscape, adversaries exploit software vulnerabilities to launch sophisticated attacks, challenging traditional defenses. Although databases like CVE and NVD provide detailed technical information, they often lack links to attacker behaviors such as tactics and techniques, limiting effective threat interpretation and response. This work bridges this gap by connecting vulnerabilities with behavioral patterns from the MITRE ATT&CK framework. We construct a CVE-TTP Knowledge Graph that links CVEs to tactics and techniques using classification and relation extraction. Transformer-based models are developed for behavior identification, with CySecBERT achieving macro F1-scores of 87.71% (techniques) and 96.16% (tactics). Also, we created an annotated dataset with 24,820 entities and 43,608 relations for entity and relation extraction. The pipeline-based approach achieves macro F1-scores of 0.86 (entity extraction) and 0.99 (relation extraction), while a span-based joint model achieves 0.78. These outputs are integrated into a Neo4j-based Cyber Threat Knowledge Graph, enabling structured visualization of vulnerabilities.
Cybersecurity Knowledge Graphs (CKGs) unify diverse Cyber Threat Intelligence (CTI) sources into structured, queryable formats, offering scalable solutions for automating proactive and real-time security responses. Their increasing adoption has significantly enhanced the workflow and decision-making efficiency of security professionals. However, constructing CKGs requires extracting entity-relation triples from unstructured CTI reports, a task hindered by complex report structure, domain-specific language, and semantic ambiguity. As a result, existing pipeline-based approaches often suffer from error propagation, reducing extraction accuracy and limiting generalizability. This paper introduces the Context-aware Threat Intelligence Knowledge Graph (CTiKG) framework, a pipeline architecture designed to accurately extract and classify threat entities and their relationships from CTI reports. CTiKG incorporates hybrid NLP models that leverage SecureBERT+ contextual embeddings and expert knowledge from a domain ontology to reduce misclassifications and mitigate cascading errors. Experiments on the DNRTI-AUG-STIX2 dataset, which comprises 21 entity types aligned with STIX 2.1, demonstrate significant improvements over state-of-the-art baselines, yielding 3-4% gains in NER and up to 8% in RE performance, based on precision, recall, and F1-score. Additional validation on DNRTI and STUCCO benchmarks confirms the framework's robustness and practical applicability. All datasets, including the curated DNRTI-AUG-STIX2, are released on GitHub to foster reproducibility and further research.
A single vulnerability in a widely used library can cascade through millions of dependent applications, yet more than half of vulnerability database entries contain missing or incorrect affected-library information. Existing automated approaches neglect the relational structure of vulnerability databases, treating identification as an isolated text retrieval problem. In this paper, we propose Athena, the first graph-based approach for vulnerability affected library identification. Athena models vulnerability databases as a knowledge graph and reformulates the identification problem as knowledge graph completion (KGC). It comprises three key modules: a Modeling module that constructs a security knowledge graph integrating CVEs, libraries, CWE weakness types, CPE products, and software ecosystems; a Completion module that applies a modular KGC backbone to predict missing affected libraries for a given CVE via link prediction; and a Re-ranking module that retrieves KGC candidates and rescores them using a fine-tuned LLM augmented with knowledge graph embeddings, jointly leveraging structural and textual information. Our experiments on VulLib demonstrate that Athena significantly outperforms four state-of-the-art baselines, achieving a 32% improvement in Avg. F1 over the best baseline (i.e., VulLibGen). Notably, our KGC backbone with only 110M parameters already surpasses VulLibGen's best configuration at 7B parameters, demonstrating the effectiveness of graph-based modeling; the re-ranking module then provides substantial further gains, consistently outperforming the best baseline across all evaluated LLM backbones.
The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches often fail to process the vast amount of unstructured textual data effectively, highlighting the need for advanced solutions. This study leverages state-of-the-art topic modeling techniques powered by large language models (LLMs) to extract meaningful insights from the 'Threat' feature of a software vulnerability dataset. Models such as BERTopic, Top2Vec, CombinedTM, Llama2 with BERTopic, and Mixtral are utilized, along with dimensionality reduction and clustering methods like UMAP, PCA, HDBSCAN, and DBSCAN. By uncovering latent patterns and generating interpretable clusters, this research enhances threat prioritization and decision-making in cybersecurity. The findings support scalable and automated solutions for vulnerability management, contributing to improved security practices.