Cyber Threat Hunting Using Large Language Models

Cyber threat hunting plays a critical role in proactively identifying and mitigating potential cybersecurity threats. The emergence of large language models (LLMs) has increased interest in the application of LLMs in cyber threat hunting. This research paper investigates the utilization of LLMs for cyber threat hunting and provides an overview of their potential benefits, challenges, and future directions. LLMs possess powerful language understanding capabilities and can process vast amounts of data, enabling them to uncover hidden patterns, detect anomalies, and identify vulnerabilities. By incorporating LLMs into workflows, organizations can enhance their ability to detect and respond to emerging threats in real time. The adoption of LLMs in threat hunting raises challenges related to bias and fairness, privacy, and computational efficiency. This paper discusses these challenges and explores potential solutions. The paper outlines future directions, such as improving LLM training with cybersecurity-specific domains and integrating contextual knowledge into LLM-based threat hunting approaches.