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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
European Alliance for Innovation (EAI)
2025-01-01
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| Series: | EAI Endorsed Transactions on Industrial Networks and Intelligent Systems |
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| Online Access: | https://publications.eai.eu/index.php/inis/article/view/7616 |
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| _version_ | 1841555880729903104 |
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| author | Muhammet Onur Kaya Mehmet Ozdem Resul Das |
| author_facet | Muhammet Onur Kaya Mehmet Ozdem Resul Das |
| author_sort | Muhammet Onur Kaya |
| collection | DOAJ |
| description |
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.
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| format | Article |
| id | doaj-art-287477c816ef41379e5822a270a20390 |
| institution | Kabale University |
| issn | 2410-0218 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Industrial Networks and Intelligent Systems |
| spelling | doaj-art-287477c816ef41379e5822a270a203902025-01-07T20:50:20ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Industrial Networks and Intelligent Systems2410-02182025-01-0112210.4108/eetinis.v12i2.7616A novel approach for graph-based real-time anomaly detection from dynamic network data listened by WiresharkMuhammet Onur Kaya0https://orcid.org/0009-0004-6313-2278Mehmet Ozdem1https://orcid.org/0000-0002-2901-2342Resul Das2Fırat University Türk Telekom (Turkey) Fırat University 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. https://publications.eai.eu/index.php/inis/article/view/7616Cyber AttacksInformation SecurityGraph VisualizationTemporal Dynamic NetworksWireshark |
| spellingShingle | Muhammet Onur Kaya Mehmet Ozdem Resul Das A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Cyber Attacks Information Security Graph Visualization Temporal Dynamic Networks Wireshark |
| title | A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark |
| title_full | A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark |
| title_fullStr | A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark |
| title_full_unstemmed | A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark |
| title_short | A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark |
| title_sort | novel approach for graph based real time anomaly detection from dynamic network data listened by wireshark |
| topic | Cyber Attacks Information Security Graph Visualization Temporal Dynamic Networks Wireshark |
| url | https://publications.eai.eu/index.php/inis/article/view/7616 |
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