NLP-based Cyber Threat Data Analysis with MITRE ATT&CK Techniques
Bibliographic record
Abstract
Recognizing and classifying attacks, threats and actors plays vital role in cyber threat intelligence (CTI). Analyzing potential threats correctly require adequate knowledge, experience and resources. Most enterprises do not have a clear classification plan after the cyberattacks to detect or prevent future threats. In this paper, we examined effective categorizing of the attacks through MITRE ATT&CK's classification to provide targeted protective measures. This article describes classifying cyberattacks via manual labeling and automated labeling process using a machine learning approach, i.e., the BiLSTM classifier, RF and LGBM model by describing them through the MITRE ATT&CK framework. Both the BiLSTM and the RF model obtained accuracies around 56%-62% while the LGBM classifier obtained higher accuracy of 99% in categorizing cyber threats and warnings using natural language processing. The ML approach can help faster automated labeling and creating good repository of MITRE ATT&CK for organizations and professionals in CTI process.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".