Network Traffic Analysis and Visualization: Statistical Evaluation of the Instrument's Performance Using ANOVA and Multidimensional Scaling Techniques

Authors

• Sudhakara Reddy Peram Senior Manager, Engineering

Abstract

Network traffic analysis has become essential for cyber security, as the rapid growth of internet-connected devices and sophisticated cyber threats necessitates advanced visualization and anomaly detection methods to transform complex data into actionable intelligence. This study addresses critical gaps in network security by integrating automated detection systems with intuitive visualization frameworks, evaluating specific protocol requirements, and developing prioritization mechanisms to minimize false positive alerts across diverse network environments. A comprehensive dataset of 750+ network traffic visualization tools was systematically evaluated across 13 assessment criteria, including traffic visualization capabilities, deployment types, and eight performance metrics rated on a 1-5 scale, encompassing both open-source and enterprise commercial platforms. Statistical analysis revealed that ease of use (mean=3.24) and reporting capabilities (mean=3.11) were highly prioritized, with enterprise commercial tools dominating at 31.8%. ANOVA results showed significant differences in media support, performance scalability, and real-time forensic analysis in enterprise environments. Balancing technical performance, user accessibility, and organizational compliance is crucial for effective network security, and future research is needed on adaptive frameworks that integrate machine learning-enhanced anomaly detection with protocol-specific visualization techniques.