Enhanced anomaly detection in network security: a comprehensive ensemble approach
https://doi.org/10.17586/2226-1494-2024-24-5-788-796
Abstract
Detection and handling of anomalous behavior in the network systems are peremptory efforts to ensure security for vulnerable infrastructures amidst the dynamic context of cybersecurity. In this paper, we propose an ensemble machine learning model architecture that leverages the strengths of XGBoost, Gradient Boosting, Random Forest, and Support Vector Machine models to identify anomalies in the dataset. This method utilizes an ensemble of these models with weighted voting based on accuracy to enhance anomaly detection for robust and adaptive real-world network security. The proposed ensemble learning model is evaluated on standard metrics and demonstrates exceptional efficacy, achieving an impressive accuracy of 99.68 % on NSL KDD dataset. This remarkable performance extends the model prowess in discerning anomalies within network traffic showcasing its potential as a robust tool for enhancing cybersecurity measures against evolving threats.
About the Authors
R. Pandey
Moscow Institute of Physics and Technology (National Research University)
Russian Federation
Russian Federation
Rashmikiran Pandey - PhD Student
Moscow region, Dolgoprudny, 141701
M. Pandey
Moscow Institute of Physics and Technology (National Research University)
Russian Federation
Russian Federation
Mrinal Pandey - PhD Student, Moscow Institute of Physics and Technology (National Research University)
Moscow region, Dolgoprudny, 141701
A. N. Nazarov
Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences
Russian Federation
Russian Federation
Alexey N. Nazarov - D.Sc., Professor
Moscow, 119333
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Review
For citations:
Pandey R., Pandey M., Nazarov A.N. Enhanced anomaly detection in network security: a comprehensive ensemble approach. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2024;24(5):788-796. https://doi.org/10.17586/2226-1494-2024-24-5-788-796
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