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Comparative Analysis of LLMs vs. Traditional Methods in Vulnerability Detection
Abstract
In the evolving landscape of cybersecurity, the detection of software vulnerabilities is paramount for ensuring system integrity and protection. This chapter provides a comparative analysis of large language models (LLMs) versus traditional methods in vulnerability detection. It explores the strengths and limitations of each approach, focusing on accuracy, efficiency, adaptability, and scalability. By examining real-world case studies and experimental results, the chapter highlights the transformative potential of LLMs in detecting complex vulnerabilities. It also discusses the implications of integrating LLMs into existing security frameworks and the challenges posed by their adoption. This analysis serves as a guide for practitioners and researchers seeking to optimize vulnerability detection methods in an increasingly dynamic threat environment.
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