Summary of Cyber-attack Technique Classification Using Two-stage Trained Large Language Models, by Weiqiu You and Youngja Park
Cyber-Attack Technique Classification Using Two-Stage Trained Large Language Models
by Weiqiu You, Youngja Park
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This AI research paper proposes a sentence classification system to identify attack techniques in cyber threat intelligence (CTI) reports, enabling security analysts to better comprehend attacker behaviors and implement effective mitigation measures. The system leverages auxiliary data with labeled examples to improve classification for the low-resource cyberattack task. The method involves training the model using augmented training data and then retraining it solely on primary data. Evaluations demonstrate that this approach boosts Macro-F1 scores by 5-9 percentage points while maintaining competitive Micro-F1 performance on the TRAM dataset, which is benchmarked against the MITRE ATT&CK framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The system helps security analysts understand attack patterns and implement effective mitigation measures. It’s like having a superpower to identify potential threats before they happen! |
Keywords
* Artificial intelligence * Classification
