Application of Natural Language Processing in Network Security Log Analysis

Abstract

Natural Language Processing (NLP), specifically, has emerged as a vital weapon against cybercrime, particularly for network log analysis. As network traffic grows ever more complex and data volumes rise exponentially, traditional log analysis methods become insufficient and advanced NLP technologies should become an area of study. This paper investigates how NLP techniques can be utilized to increase efficiency and effectiveness in network security log analysis, specifically with regard to parsing logs automatically as well as anomaly detection. It explores whether automation could enable NLP techniques for any better analysis results. Leveraging NLP makes it possible to quickly and accurately analyze logs by turning unstructured, complex data into usable insights. Studies demonstrate this effect. Reduce time taken to detect and respond to potential threats; adopt proactive network security management practices. This paper emphasizes the value of using machine learning models combined with NLP techniques in adapting to new and evolving attacks, and providing a robust defense mechanism. Furthermore, challenges and future research directions related to this area are explored as part of this discussion.