This paper surveys the potential of contextualized AI in enhancing cyber defense capabilities, revealing significant research growth from 2015 to 2024. We identify a focus on robustness, reliability, and integration methods, while noting gaps in organizational trust and governance frameworks. Our study employs two LLM-assisted literature survey methodologies: (A) ChatGPT 4 for exploration, and (B) Gemma 2:9b for filtering with Claude 3.5 Sonnet for full-text analysis. We discuss the effectiveness and challenges of using LLMs in academic research, providing insights for future researchers.
@article{arxiv.2409.13524,
title = {Contextualized AI for Cyber Defense: An Automated Survey using LLMs},
author = {Christoforus Yoga Haryanto and Anne Maria Elvira and Trung Duc Nguyen and Minh Hieu Vu and Yoshiano Hartanto and Emily Lomempow and Arathi Arakala},
journal= {arXiv preprint arXiv:2409.13524},
year = {2025}
}
Comments
8 pages, 2 figures, 4 tables, accepted into 17th International Conference on Security of Information and Networks (SINCONF 2024)