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A Comprehensive Classification Approach by Integrating Principal Component Analysis and Support Vector Machines for Advanced Intrusion Detection Systems

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Abstract

In cybersecurity, machine learning-based detection has emerged as a pivotal approach for discovering and mitigating various cyber dangers. The proposed PCA-SVM model demonstrates significant proficiency in distinguishing between normal and anomalous system access behaviors. By leveraging PCA for feature reduction and SVM for classification, the model achieves superior detection rates compared to existing intrusion detection methods. The dataset used in this research comprises two distinct classes, “Normal Location” and “Anomalous Location.” To generate a comprehensive dataset, synthetic data was created using Generative Adversarial Networks (GANs), focusing on longitude and latitude as the primary attributes representing location. This synthetic approach ensures a diverse and extensive dataset, essential for training a robust model. This indicates the effectiveness of the PCA-SVM approach in identifying and preventing a wide range of cyber threats. The optimistic results of this research highlight the potential for further research and development in the field, aiming to refine and expand upon the PCA-SVM integration for even greater efficacy in cyber threat detection.

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Data Availability

The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The author would like to thank REVA University, Bengaluru, Karnataka, India for their encouragement and support in carrying out this research work.

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Correspondence to C. Pavithra.

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Pavithra, C., Saradha, M. A Comprehensive Classification Approach by Integrating Principal Component Analysis and Support Vector Machines for Advanced Intrusion Detection Systems. SN COMPUT. SCI. 5, 996 (2024). https://doi.org/10.1007/s42979-024-03308-z

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