A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark

Abstract

This paper presents a new approach for real-time anomaly detection and visualization of dynamic network data using Wireshark, known as the most widely used network analysis tool. As the complexity and volume of network data increases, effective anomaly detection has become important to maintain network performance and improve security. The proposed method uses comprehensive network packet information from Wireshark for fast and accurate anomaly detection. The collected network packets enable easy detection and interpretation of anomalies in a dynamic manner. The presentation is enriched with graph-based representations. Thanks to this method, network administrators can understand the data more easily and make more sound decisions. The results of our study show significant improvements in the effectiveness of anomaly detection and practical applicability of visualization tools in real-time scenarios. The present study integrates the advances in the network approach and various visualization methods to provide the new ideas for network security and management for dynamic network management improvement. Copyright © 2025 M. O. Kaya et al., licensed to EAI.