Author

Ying-Dar Lin

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2026

Reinforcement Learning Meets LLM Honeypots: A MITRE Engage-Aligned Approach

The growing sophistication of cyberattacks, accelerated by large language models (LLMs), highlights the limitations of traditional honeypots, which often lack realism, require heavy maintenance, and rely on static deception strategies. Recent LLM-based honeypots generate fluent, context-aware responses but cannot adapt...

Ren-Hung Hwang, Jiao-Chuan Huang, Yuan-Cheng Lai et al. · IEEE Transactions on Network... · 0 citations

Open access 2026

LLM-Driven Alert Correlation and Lifecycle Prediction With Threat Intelligence for Attack Forensics

Security Operations Centers face overwhelming volumes of alerts from firewalls, intrusion detection systems, and other sources. These alerts often lack temporal and semantic coherence, hindering analysts from reconstructing full MITRE ATT&CK lifecycles or anticipating subsequent techniques. To address this, we propose...

Ying-Dar Lin, Chun-Yi Chang, Didik Sudyana et al. · IEEE Transactions on Machine... · 0 citations