Network Traffic Analysis To Classify Malicious And Non-Malicious Traffic

Abstract

In the face of increasingly sophisticated cyber threats, ensuring network security is crucial for organizations aiming to protect sensitive data, maintain service continuity, and avoid financial losses. Effective network traffic monitoring is essential for identifying malicious activities that can compromise network integrity. Traditional methods, however, often struggle to keep up with evolving attack techniques, especially when real-time detection and rapid response are needed. This project presents an innovative network traffic analysis system that integrates the capabilities of Wireshark, machine learning, and Grafana. Wireshark provides in-depth packet inspection, while machine learning enables automatic traffic classification and anomaly detection, offering a proactive approach to threat identification beyond traditional rule-based methods. Grafana’s customizable real-time visualization displays the analyzed data, providing network administrators with a clear, accessible view to identify patterns and make informed security decisions. This unified approach delivers a scalable, comprehensive solution for modern network environments, enhancing real-time threat detection while minimizing false positives and empowering organizations to fortify their cybersecurity defences effectively.