Harnessing the Advanced Capabilities of LLM for Adaptive Intrusion Detection Systems

The integration of Machine Learning (ML) and Deep Learning (DL) techniques has significantly advanced Intrusion Detection Systems (IDSs) across diverse domains such as networking, cybersecurity and industrial control systems. These approaches have played a key role in the development of Artificial Intelligence (AI) within IDSs. Moreover, the emergence of Large Language Models (LLMs) has gained prominence, encompassing both ML and AI capabilities. These models are trained on extensive datasets, enabling them to generate human-like text generation and autonomous decisions taking. This paper is intended to evaluate the capacity of LLMs in the context of IDSs for networking. LLMs exhibit the ability to process and comprehend large volumes of network log data, autonomously learn, adapt to evolving network behavior, and effectively differentiate between regular activities and potential threats. Emphasizing the substantial role of ML and AI by enhancing the adaptability and performance of IDSs technologies. The present work also underscores the potential of LLMs and their fine-tuning to reinforce IDSs capabilities, while addressing the associated challenges.