Automated Identification and Forensic Analysis of Network Traffic Anomalies Through Ensemble Learning Techniques: An Advanced Machine Learning Frame Work for Cybersecurity Threat Intelligence
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2025
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| _version_ | 1866901396695023616 |
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| author | Emmanuel Burma Usoro Edidiong Michael Etuk |
| author_facet | Emmanuel Burma Usoro Edidiong Michael Etuk |
| contents | In the era of increasing cyber threats, automatically identifying and analyzing anomalies within network traffic is essential for robust cybersecurity intelligence. This study explores the application of ensemble learning methods to enhance anomaly detection in network traffic. The dataset underwent thorough preprocessing and descriptive statistical analysis, confirming the proper normalization of key features. Among the ensemble models tested, AdaBoost achieved a strong overall accuracy of 0.89, with high precision (0.90) and recall (0.99) for normal traffic classification. XGBoost also performed effectively, with an accuracy of 0.88, showcasing its capability to analyze complex network behaviors. The proposed framework establishes a solid foundation for integrating intelligent systems into cybersecurity infrastructures, supporting proactive anomaly detection and in-depth forensic analysis. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18104052 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | Automated Identification and Forensic Analysis of Network Traffic Anomalies Through Ensemble Learning Techniques: An Advanced Machine Learning Frame Work for Cybersecurity Threat Intelligence Emmanuel Burma Usoro Edidiong Michael Etuk Machine Learning Network Threat Anomaly detection Forensic Analysis In the era of increasing cyber threats, automatically identifying and analyzing anomalies within network traffic is essential for robust cybersecurity intelligence. This study explores the application of ensemble learning methods to enhance anomaly detection in network traffic. The dataset underwent thorough preprocessing and descriptive statistical analysis, confirming the proper normalization of key features. Among the ensemble models tested, AdaBoost achieved a strong overall accuracy of 0.89, with high precision (0.90) and recall (0.99) for normal traffic classification. XGBoost also performed effectively, with an accuracy of 0.88, showcasing its capability to analyze complex network behaviors. The proposed framework establishes a solid foundation for integrating intelligent systems into cybersecurity infrastructures, supporting proactive anomaly detection and in-depth forensic analysis. |
| title | Automated Identification and Forensic Analysis of Network Traffic Anomalies Through Ensemble Learning Techniques: An Advanced Machine Learning Frame Work for Cybersecurity Threat Intelligence |
| topic | Machine Learning Network Threat Anomaly detection Forensic Analysis |
| url | https://doi.org/10.5281/zenodo.18104052 |