Applying the Diamond Model of Intrusion Analysis with Generative Pre-trained Transformer 3
Sheng-Shan Chen, Tun‐Wen Pai, Chin‐Yu Sun · 2023
Since 2020, the number of cyber-attacks has doubled, seriously affecting the quality of private internet use and causing significant economic losses. Threat intelligence analysts use threat reports to analyze malicious actors and organizations to prevent cyber-attacks. This study proposes a method that leverages the diamond model of intrusion analysis and deep learning models to provide a comprehensive view of malware attacks. We examine the comparative efficacy of Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transfer (GPT) in threat intelligence reporting. The results show that GPT achieved an accuracy rate of 61%, a 17% improvement over BERT. It can effectively assist malware analysts in their decision-making process.