International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The rapid growth of network-based services has increased the exposure of modern communication infrastructures to a wide range of cyber attacks, making accurate and timely intrusion detection a critical requirement. Traditional rule-based security mechanisms often struggle to detect evolving and multi-type network attacks due to their reliance on predefined signatures. This paper presents a machine learning–based framework for the prediction and classification of multi-type network attacks using time-based traffic features. The proposed system analyzes temporal characteristics of network flows, including packet inter-arrival times, flow duration, and active–idle behavior, to distinguish benign traffic from malicious activities and further classify attacks into specific categories. A trained machine learning model processes network traffic data provided in CSV format and performs multi-class attack classification with high accuracy. An interactive dashboard developed using Python Dash enables users to upload traffic data, execute predictions, and visualize results through charts and detailed tables. Experimental evaluation demonstrates that time-based feature analysis significantly enhances detection performance compared to conventional approaches, while providing an automated, scalable, and user-friendly solution for network security monitoring.

Keywords: Network Attack Detection, Time-Based Traffic Features, Machine Learning, Multi-Class Classification, Intrusion Detection System, Network Security Dashboard

Downloads: | DOI: 10.17148/IJARCCE.2026.15150