Volume 2 Issue 2 | 2025 | View PDF
Paper Id:IJMSM-V2I2P101
doi: 10.71141/30485037/V2I2P101

Detecting Unbalanced Network Traffic : A Machine Learning Using Stacked Generalization

Vitthal B. Kamble, Kunal R. Jadhav, Tanesh M. Patil, Ayush D. More

Citation:
Vitthal B. Kamble, Kunal R. Jadhav, Tanesh M. Patil, Ayush D. More, "Detecting Unbalanced Network Traffic : A Machine Learning Using Stacked Generalization" International Journal of Multidisciplinary on Science and Management, Vol. 2, No. 2, pp. 1-16, 2025.

Abstract:
Cyber Threats are becoming more frequent and sophisticated, so we need better systems to detect malicious activities in network traffic. Detecting unbalanced network traffic is a critical challenge in cyber security, where malicious activities are often underrepresented in comparison to legitimate traffic. This study proposes a hybrid approach using XG- Boost, Random Forest, and an ensemble model to effectively identify anomalies in network traffic data. We also use dataset of IDS. Our approach improves the accuracy, efficiency, and reliability of intrusion detection, contributing to stronger defenses against cyber attacks and protecting important network systems.

Keywords: Cyber security, Intrusion Detection System (IDS), XG-Boost, Random Forest, Ensemble Model, Anomaly Detection, Class Imbalance, Network Security, Machine Learning, Hybrid Model, Threat Detection, Data Preprocessing, Malicious Traffic Detection.

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