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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST)
Dedy Kiswanto
Program Studi Ilmu Komputer, Universitas Negeri Medan
Computer Science & IT

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AI-Based Wi-Fi Traffic Profiling and Anomaly Detection System Micael Zecsen Saragih; Dedy Kiswanto; Jhon Gabriel Simarmata; Bryant Tinambunan
IJCONSIST JOURNALS Vol 8 No 1 (2026): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v8i1.207

Abstract

Abstract— Network traffic monitoring plays an important role in maintaining the availability, reliability, and performance of modern network infrastructures. The increasing volume and complexity of network traffic require monitoring solutions that provide real-time visibility, traffic profiling, anomaly detection, and comprehensive usage analysis. This study proposes an AI-based Wi-Fi network traffic monitoring and analysis system that integrates ntopng for traffic collection, Python-based analytical modules, Prometheus for metrics aggregation, Grafana for visualization, K-Means Clustering for traffic profiling, and Isolation Forest for anomaly detection. The system was implemented in a wireless network environment using real network traffic data collected through the ntopng REST API. The collected traffic data were analyzed to identify traffic patterns, assess traffic severity, categorize network usage activities, and generate monitoring metrics for real-time visualization. The results demonstrate that the proposed system successfully performs traffic clustering, anomaly detection, usage categorization, dashboard-based monitoring, and automated alert integration. Furthermore, the generated analytical information provides insights into network utilization patterns that may support future Quality of Service (QoS) implementation, bandwidth allocation strategies, and intelligent network management. The integration of machine learning techniques with modern monitoring platforms contributes to more effective and centralized network monitoring.
Co-Authors Bryant Tinambunan Jhon Gabriel Simarmata Micael Zecsen Saragih