A Systematic Comparison of Large Language Models Performance for Intrusion Detection
Minh-Thanh Bui, Matteo Boffa, Rodolfo Valentim, Jose Manuel Navarro, Fuxing Chen, Xiaosheng Bao, Zied Ben Houidi, Dario Rossi
Abstract
We explore the capabilities of Large Language Models (LLMs) to assist or substitute devices (i.e., firewalls) and humans (i.e., security experts) respectively in the detection and analysis of security incidents. We leverage transformer-based technologies, from relatively small to foundational sizes, to address the problem of correctly identifying the attack severity (and accessorily identifying and explaining the attack type). We contrast a broad range of LLM techniques (prompting, retrieval augmented generation, and fine-tuning of several models) using state-of-the-art machine learning models as a baseline. Using proprietary data from commercial deployment, our study provides an unbiased picture of the strengths and weaknesses of LLM for intrusion detection.