Research on the Application of Large Language Models in Cybersecurity

With the rapid development of information technology, cybersecurity is facing increasingly complex challenges, and traditional security measures are gradually failing to meet the diversity and high frequency of modern cyberattacks. Large Language Models (LLMs), as an advanced deep learning-based technology, have achieved significant results in the field of natural language processing (NLP), and their potential applications in cybersecurity are gaining attention. This paper explores the applications of LLMs in cybersecurity, focusing on their practices and effects in threat detection, anomaly detection, and security event response. By comparing with traditional methods, the paper analyzes the advantages and limitations of LLMs in improving security, automating responses, and predicting malicious activities. Furthermore, the paper evaluates model performance based on experimental data and discusses the challenges LLMs face in cybersecurity applications, such as data privacy issues, computational resource consumption, and model deployment complexity. Finally, the paper looks forward to the future development and potential of LLMs in cybersecurity defense, emphasizing the integration of multimodal data and intelligent decision systems.