Enhancing anomaly detection for social multimedia in SDN using glow worm-driven deep belief network
The evolution in network security within social multimedia communication depends on effective anomaly detection. One of the most essential elements of security involves identifying suspicious behavior during multimedia data transport. These abnormalities can adversely impact the network's perfo...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-07-01
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| Series: | Automatika |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2476806 |
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| author | S. K. Manju Bargavi Chintan Thacker Varsha Agarwal Yaduvir Singh Sneha Kashyap Dhananjay Kumar Yadav |
| author_facet | S. K. Manju Bargavi Chintan Thacker Varsha Agarwal Yaduvir Singh Sneha Kashyap Dhananjay Kumar Yadav |
| author_sort | S. K. Manju Bargavi |
| collection | DOAJ |
| description | The evolution in network security within social multimedia communication depends on effective anomaly detection. One of the most essential elements of security involves identifying suspicious behavior during multimedia data transport. These abnormalities can adversely impact the network's performance and reliability. Proactive anomaly detection and mitigation in social multimedia communication is rendered possible by the combination of Software-Defined Networking and advanced analytics. By identifying anomalous behavior in the transfer of social multimedia data, the technique improves security. In this paper, suggested a Glow Worm-Driven Deep Belief Network (GW-DDBN) to enhance anomaly detection, and Principal Component Analysis is used for dimensionality reduction. To apply the shortest path routing to transmit the component in the control layer. To gathered 400 real-time users’ dataset who were active on the internet for one hour and used multiple digital media platforms encompasses Facebook, Instagram, WhatsApp, and Twitter. As a result, to assess the performance of the suggested method as it relates to packet counts over some time, latency, bandwidth, and consuming energy, then compare our proposed method to the existing method concentrated on metrics including accuracy (98%), precision (97%) and recall (95%), F1 score (85.6%). The method demonstrates superior performance in identifying and addressing network irregularities. |
| format | Article |
| id | doaj-art-a0a280035e8f477792c3afb7df56b963 |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-a0a280035e8f477792c3afb7df56b9632025-08-20T03:33:11ZengTaylor & Francis GroupAutomatika0005-11441848-33802025-07-01663203010.1080/00051144.2025.2476806Enhancing anomaly detection for social multimedia in SDN using glow worm-driven deep belief networkS. K. Manju Bargavi0Chintan Thacker1Varsha Agarwal2Yaduvir Singh3Sneha Kashyap4Dhananjay Kumar Yadav5Department of Computer Science and Information Technology, Jain (Deemed to be University), Bangalore, Karnataka, IndiaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, IndiaDepartment of ISME, ATLAS SkillTech University, Mumbai, Maharashta, IndiaDepartment of Computer Science and Engineering (AI), Noida Institute of Engineering & Technology, Greater Noida, Uttar Pradesh, IndiaDepartment of Computer Science, ARKA JAIN University, Jharkhand, IndiaMaharishi School of Engineering & Technology, Maharishi University of Information Technology, Uttar Pradesh, IndiaThe evolution in network security within social multimedia communication depends on effective anomaly detection. One of the most essential elements of security involves identifying suspicious behavior during multimedia data transport. These abnormalities can adversely impact the network's performance and reliability. Proactive anomaly detection and mitigation in social multimedia communication is rendered possible by the combination of Software-Defined Networking and advanced analytics. By identifying anomalous behavior in the transfer of social multimedia data, the technique improves security. In this paper, suggested a Glow Worm-Driven Deep Belief Network (GW-DDBN) to enhance anomaly detection, and Principal Component Analysis is used for dimensionality reduction. To apply the shortest path routing to transmit the component in the control layer. To gathered 400 real-time users’ dataset who were active on the internet for one hour and used multiple digital media platforms encompasses Facebook, Instagram, WhatsApp, and Twitter. As a result, to assess the performance of the suggested method as it relates to packet counts over some time, latency, bandwidth, and consuming energy, then compare our proposed method to the existing method concentrated on metrics including accuracy (98%), precision (97%) and recall (95%), F1 score (85.6%). The method demonstrates superior performance in identifying and addressing network irregularities.https://www.tandfonline.com/doi/10.1080/00051144.2025.2476806 |
| spellingShingle | S. K. Manju Bargavi Chintan Thacker Varsha Agarwal Yaduvir Singh Sneha Kashyap Dhananjay Kumar Yadav Enhancing anomaly detection for social multimedia in SDN using glow worm-driven deep belief network Automatika |
| title | Enhancing anomaly detection for social multimedia in SDN using glow worm-driven deep belief network |
| title_full | Enhancing anomaly detection for social multimedia in SDN using glow worm-driven deep belief network |
| title_fullStr | Enhancing anomaly detection for social multimedia in SDN using glow worm-driven deep belief network |
| title_full_unstemmed | Enhancing anomaly detection for social multimedia in SDN using glow worm-driven deep belief network |
| title_short | Enhancing anomaly detection for social multimedia in SDN using glow worm-driven deep belief network |
| title_sort | enhancing anomaly detection for social multimedia in sdn using glow worm driven deep belief network |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2025.2476806 |
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