Dynamic Analysis and Visualization of Network Traffic Patterns using Machine Learning and Seasonal Decomposition Techniques

The exponential increase of network traffic due to devices running IoT, cloud computing, and remote working has added a dimension to demand high efficiency in network resource management and security. Based on this context, this article presents an integrated approach which combines the machine learning-based approach with statistical methods towards dynamic analysis and visualization of patterns in network traffic. Exploiting Dynamic Time Warping (DTW), along with clustering algorithms, the research identifies patterns in transmission and thereby throws out more insight into time-domain dynamics and anomalies. Seasonal decomposition identifies cyclical trends and seasonality, while machine learning-based anomaly detection, more specifically Isolation Forest, identifies outliers and potential threats. The methodology uses Python libraries such as Pandas, Scikit-learn, and Statsmodels to process, analyze, and visualize data from Packet Capture (PCAP) files. Network traffic behaviors are visualized using scatter plots, clustering maps, and seasonal decomposition graphs, which give actionable insights to network administrators. Results show that there are distinct patterns in the clusters of traffic, cyclical trends in bandwidth usage, and irregular anomalies in certain protocols and time windows. These findings inform proactive resource allocation and enhance network monitoring strategies. This research offers a scalable solution that bridges raw data with intuitive visualizations for real-time network traffic analysis. This is to optimize network performance, ensure reliability, and strengthen security in a constantly evolving digital landscape.