Abstract
Network security is a critical aspect of information technology aimed at protecting data and systems from cyber threats. Statistical approaches play a key role in detecting anomalies, measuring efficiency, and predicting security risks. This paper explores the intersection between network security and statistics, emphasizing the use of data analysis and statistical methods to enhance system security. Furthermore, it discusses the challenges posed by big data processing and highlights the importance of machine learning in supporting adaptive security systems. The findings suggest that integrating traditional statistical methods with modern machine learning techniques can improve real-time threat detection and risk management in network security.