A Comprehensive Review of Large Language Models in Cyber Security

Abstract

In response to the escalating complexity of cyber threats and the rapid expansion of digital environments, traditional detection models are proving increasingly inadequate. The advent of Large Language Models (LLMs) powered by Natural Language Processing (NLP) represents a transformative advancement in cyber security. This review explores the burgeoning landscape of LLM applications in cyber security, highlighting their significant potential across various threat detection domains. Recent advancements have demonstrated LLMs' efficacy in enhancing tasks such as cyber threat intelligence, phishing detection, anomaly detection through log analysis, and more. By synthesizing recent literature, this paper provides a comprehensive overview of how LLMs are reshaping cyber security frameworks. It also discusses current challenges and future directions, aiming to guide researchers and practitioners in leveraging LLMs effectively to fortify digital defences and mitigate evolving cyber threats.