Yi-Hsien Chen earned his B.S. degree in Computer Science at National Chiao Tung University (NCTU) and is currently a Ph.D. candidate in the Department of Electrical Engineering at National Taiwan University (NTU), a research assistant in the Department of Computer Science at National Yang Ming Chiao Tung University (NYCU), and a security researcher for the CyCraft research team. His research focuses on automatic malware analysis techniques and various cybersecurity topics. He utilizes symbolic execution, machine learning, and other techniques to improve malware analysis efficiency. His work has been published in IEEE TIFS, DSC, ACM ASIACCS, and CCSW. He has also spoken at HITB CyberWeek, AVTokyo, HITCON, SECCON, and CODE BLUE. Additionally, he was a member of the BambooFox CTF team from NYCU, has participated in several CTFs, and has won 12th and 2nd place in DEFCON 26 and 27, respectively, with BFS and BFKinesiS CTF teams.
Cyber Threat Intelligence (CTI) plays a pivotal role in modern cybersecurity defense, providing critical insights into vulnerabilities, attacker profiles, attack tools, and Indicators of Compromise (IoCs). However, the traditional practice of analysts relying on unstructured text for report writing, while beneficial for interpersonal communication, results in inefficient and time-consuming intelligence management.
Despite STIX format and MITRE ATT&CK® matrix providing foundational infrastructure for standardized intelligence management, their high technical barriers have hindered widespread adoption. Our solution leverages Large Language Models to develop automated tools—CTI2STIX and CTI2MITREATT&CK—enabling seamless conversion from natural language intelligence to structured formats.
Furthermore, our system integrates multi-source intelligence reports, breaking down information silos and enhancing the comprehensiveness, efficiency, and accuracy of threat analysis, thereby providing organizations with more robust cybersecurity protection capabilities.
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