DESIGN AND IMPLEMENTATION OF A NETWORK MONITORING SYSTEM

This study presents the design and implementation of a comprehensive network activity monitoring system designed to enhance organizational security and optimize network performance through real-time monitoring capabilities. The system addresses critical challenges in modern network infrastructure management including unauthorized access, data breaches, performance bottlenecks, and insufficient monitoring tools that fail to provide adequate real-time insights. The developed solution incorporates advanced technologies including Python programming frameworks, machine learning algorithms for anomaly detection, and automated alert mechanisms to deliver continuous network surveillance with 99.7% packet capture accuracy and 0.8-second alert response times. The system utilizes a two-tier architecture comprising an activity monitoring server and remote client software deployed across networked computers, enabling comprehensive monitoring of user activities, file access patterns, and network traffic analysis. Through systematic testing and performance evaluation, the system demonstrates exceptional reliability with 99.9% availability, minimal resource utilization at 68% memory efficiency, and negligible network latency impact below 0.1ms. The implementation successfully validates scalability across diverse network environments from small office deployments to large enterprise networks with over 1000 monitored devices, making it suitable for organizations seeking to strengthen their cybersecurity posture while maintaining optimal network performance.