Author

Adrian Taylor

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Review Open access Aug 2026

From static tasks to dynamic reasoning: a characterization framework and study of large language models in next-generation cybersecurity automation

This work surveys recent LLM-based systems across seven core domains and identifies the need for privacy-aware deployment, timely retrieval and knowledge maintenance for emerging threats, process-level evaluation tied to measurable security outcomes, and human oversight within controlled and hybrid automation workflows...

Hanxin Yu, Shahrear Iqbal, E. C. Pinto et al. · International Journal of Inf... · 0 citations

2026

Cyber Task Automation With Knowledge-Infused Reinforcement Learning and LLM-Guided Policies

As cyber threats continue to evolve, there is a need for Autonomous Cyber Defense (ACD) strategies capable of fast and context-aware responses. Reinforcement learning (RL) has shown promise in automating cyber defense by exploring and learning effective countermeasures. However, RL often struggles with sparse reward si...

Md. Shamim Towhid, Shahrear Iqbal, E. C. Pinto et al. · IEEE Transactions on Network... · 0 citations

Jul 2026

Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations

The use of a Large Language Model (LLM) to improve autonomous defensive decision-making within an ACO environment is investigated and an online policy distillation framework is proposed that transfers the LLM's defensive policy into a lightweight RL agent containing only 64,910 parameters, reducing model size by severa...

Konur Tholl, F. Rivest, Mariam El Mezouar et al. · arXiv.org · 0 citations