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

This paper presents a novel approach for real-time anomaly detection and visualization of dynamic network data using Wireshark, globally's most widely utilized network analysis tool. As the complexity and volume of network data continue to grow, effective anomaly detection has become essential for maintaining network performance and enhancing security. Our method leverages Wireshark’s robust data collection and analysis capabilities to identify anomalies swiftly and accurately. In addition to detection, we introduce innovative visualization techniques that facilitate the intuitive representation of detected anomalies, allowing network administrators to comprehend network conditions and make informed decisions quickly. The results of our study demonstrate significant improvements in both the efficacy of anomaly detection and the practical applicability of visualization tools in real-time scenarios. This research contributes valuable insights into network security and management, highlighting the importance of integrating advanced analytical methods with effective visualization strategies to enhance the overall management of dynamic networks.