Network Traffic Analysis Using Machine Learning - Parvat, Thaksen; Kolwankar, Siddhi; Lopes, Neel; Sawant, Prathamesh; Parchurkar, Shreya (Indian Journal of Computer Science, 2024)
In today’s interconnected world, effective network traffic analysis and security are vital. Our goal is to utilize Machine Learning for real-time analysis of network traffic, providing actionable insights and solutions to both users and network administrators. This involves classifying packets into different applications and identifying anomalies including malware patterns, at both the Transport and Application layers. We are committed to enhancing and updating our user-friendly interface to enable efficient monitoring of network traffic, empowering users to stay informed about network applications and security threats. By continually adapting to evolving network malware and patterns, our system ensures efficiency and effectiveness. Through prioritizing user experience and providing real-time insights, our application addresses the dual challenges of network traffic analysis and cyber security. We recognize the importance of proactive monitoring and resource management in an ever changing digital environment, ultimately contributing to the efficiency and security of networked environments.