Leveraging LLM for Zero-Day Exploit Detection in Cloud Networks

Abstract

Cloud network security faces challenges due to the increasing complexity and evolving nature of cyber threats, rendering traditional rule-based monitoring systems inadequate. This paper explores the potential of Large Language Models (LLMs) to revolutionize cloud security by addressing the limitations of rule-based approaches. We investigate how LLMs can enhance anomaly detection, generate actionable threat intelligence, and automate incident response processes. Through real-world examples and case studies, we demonstrate the practical applications of LLMs in fortifying cloud network security. However, we also acknowledge the challenges and ethical considerations associated with LLM deployment, such as hallucinations, bias, and privacy concerns. We propose strategies to mitigate these risks and emphasize the importance of human oversight in LLM-driven security systems. This comprehensive review underscores the significance of LLMs in shaping the future of cloud network security and provides valuable insights for researchers, practitioners, and decision-makers in this rapidly evolving field.