Leveraging Large Language Models for Threat Detection and Cyber Defence: A Framework for Automated Security Analytics

Abstract

In today’s rapidly evolving digital landscape, the volume and sophistication of cyber threats require innovative approaches to threat detection and cyber defence. Traditional security systems, while effective to a degree, are increasingly challenged by complex attack vectors that exploit vulnerabilities across multiple technology layers. This study introduces an advanced framework that leverages large language models (LLMs) for automated security analytics, offering a transformative solution to modern cybersecurity challenges. By integrating cutting-edge natural language processing techniques with machine learning algorithms, the framework systematically analyzes diverse data streams—including system logs, security alerts, and threat intelligence reports—to detect anomalous behaviors and subtle indicators of compromise. The contextual comprehension provided by LLMs enables the identification of patterns that conventional methods often miss, thereby enhancing both the precision and responsiveness of threat detection processes. Rigorous experimental evaluations demonstrate that the adoption of LLMs significantly improves detection accuracy while reducing the time required to respond to emerging threats. Additionally, the framework is engineered for continuous learning, ensuring that it adapts to evolving cyber adversaries and new attack strategies over time.