Research on TTP Data Augmentation Methods Based on the ATT&CK Framework

As cyber threats escalate, rapid identification and response to attacks are increasingly vital. Cyber Threat Intelli-gence (CTI) is crucial for understanding the threat landscape, and standardized attack frameworks are essential for effective anal-ysis. The MITRE ATT &CK framework has gained widespread adoption for its systematic description of Tactics, Techniques, and Procedures (TTP), aiding security teams in tracking at-tack patterns. However, manual classification of TTP is time-consuming and costly, hindering response efficiency. Although artificial intelligence has advanced automated TTP classification, accuracy still needs improvement due to the scarcity of labeled data, resulting in small and imbalanced datasets. This study introduces a novel TTP data augmentation method to enhance classification accuracy through synthetic data gen-eration. We construct a dataset of 19,716 sentences from the ATT&CK knowledge base and real-world threat reports, Ini-tially, we leverage large language models (LLMs) combined with prompt techniques to generate high-quality synthetic data, followed by semantic filtering and dynamic sampling strategies to further enhance data quality and improve class balance. Experimental results show an average $\mathbf{F}_{1}$ score increase of 16.95 % across various classification models, significantly enhancing TTP classification performance.