Entity and relation extractions for threat intelligence knowledge graphs
Bibliographic record
Abstract
Advanced persistent threats (APTs) represent a complex challenge in cybersecurity as they infiltrate networks stealthily to conduct espionage, steal data, and maintain a long-term presence. To combat these threats, security professionals increasingly rely on cyber knowledge graphs (CKGs), which provide scalable solutions to analyze and structure vast amounts of cyber threat intelligence (CTI) from diverse sources in real-time, enabling the automation of proactive security measures. Developing CKGs requires extracting entity and their relationships from unstructured CTI reports. However, existing approaches face significant limitations, such as difficulties with the nuances of cybersecurity language, diverse threat terminologies, and high rates of error propagation, resulting in low accuracy and poor generalizability. This paper introduces a novel Threat Intelligence Knowledge Graph (TiKG) pipeline designed to address these challenges. The TiKG framework leverages SecureBERT, a domain-specific transformer-based model optimized for cybersecurity, and integrates it with an attention-based BiLSTM to capture the context and nuances of security texts, reducing error propagation and improving extraction accuracy. Additionally, the pipeline incorporates a domain-specific ontology and inference model to ensure precise relation mapping in relation extraction. Using three large-scale TI open-source datasets (DNRTI, STUCCO, and CYNER) and a curated CTI dataset, extensive evaluations demonstrate the effectiveness of our framework, showing significant improvements over existing methods in detecting and linking cyber threats. These contributions provide a robust platform for security professionals to analyze and predict potential attacks, develop effective defenses, and enhance the strategic capabilities of cybersecurity operations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".