LEVERAGING LARGE LANGUAGE MODELS IN CYBERSECURITY: A SYSTEMATIC REVIEW OF EMERGING METHODS AND TECHNIQUES

Abstract

This systematic literature review examined how Large Language Models (LLMs) can be incorporated with vulnerability scanning and other cybersecurity tools and explored and assessed ways to improve cybersecurity practices. The PRISMA model was used, and the search was conducted using specific search terms in the leading databases such as the ACM Digital Library, IEEE Xplore Digital Library, and ScienceDirect from 2018 to July 2024. Initially, 313 records were gathered and reduced the count was reduced to 48 articles after applying the inclusion criteria. The findings were structured to answer the research questions regarding the approaches applied to incorporate LLMs with cybersecurity tools and the strengths and limitations of these tools based on the identified methodologies. The methods were reviewed and classified into Training and Adaptation Methods, Integration and Deployment Methods, and Inference and Utilization Techniques. After that, the accuracies of these methods were presented. The results show that fine-tuning and domain adaptation improves LLMs’ performance in cybersecurity tasks. In addition, fine-tuning, prompt engineering, and few-shot learning enhance models for specific tasks, making them more efficient in practical applications.