Leveraging Large Language Models to Enhance Cybersecurity Defenses Against Sophisticated Cyber Threats

Abstract

In our hyper-connected world, cyber threats are becoming more sophisticated by the day, making it increasingly difficult for traditional security methods to keep up. This thesis delves into the potential of Large Language Models (LLMs)—such as BERT and GPT—to transform the way we defend against these evolving threats. LLMs are not just capable of identifying threats; they also enable real-time incident responses, giving organizations the power to stop attacks like ransomware, DDoS, phishing, and SQL injection before they can cause serious damage. Our study leverages well-known datasets like UNSW-NB15, CICFlowMeter, and custom cyber-operations data to train these advanced models. Through extensive testing and evaluation using metrics such as accuracy and adaptability, we found that LLMs consistently outperform traditional detection methods. What sets this research apart is the integration of real-time response mechanisms, allowing the system to react instantly to potential threats—whether it’s isolating a compromised system or blocking malicious traffic—making cybersecurity defenses more proactive and adaptive. This work demonstrates that LLMs offer a powerful and scalable solution for today’s cybersecurity challenges, helping organizations stay one step ahead of attackers. As cyber threats continue to evolve, the ability of these models to learn, adapt, and respond dynamically positions them as essential tools in modern cybersecurity strategies.