Network Traffic Analysis and Optimization Using Network Analyzers: A Comparative Study

With the increasing reliance on digital infrastructure, cloud computing, IoT and 5G technologies, network traffic analysis has become a critical aspect of maintaining cybersecurity, optimizing performance and operational efficiency. This study presents a comprehensive comparative evaluation of various network traffic analyzers and optimization approaches with a focus on their applicability in modern network environments, including enterprise systems, cloud platforms and IoT-based infrastructures. The study highlights the evolving nature of network security challenges and the need for advanced, scalable and adaptive monitoring tools to detect anomalies, manage bandwidth and respond to cyber threats in real-time. Several network analyzers are considered, including traditional packet analysis tools such as Wireshark, machine learning-based intrusion detection systems, IoT-based traffic monitoring solutions and flow-based protocols such as NetFlow and sFlow. Each method is evaluated based on key performance metrics such as accuracy, efficiency, scalability and security. The article then examines emerging technologies shaping the future of network traffic analysis, such as blockchain for decentralized security, quantum computing for next-generation encryption, and edge computing for distributed real-time monitoring. Optimization strategies, including load balancing, QoS, traffic shaping, and software-defined networking (SDN), are also discussed for their role in ensuring reliable and high-performance network operations.