Orthogonal variance-based feature selection for intrusion detection systems

TL;DR

This paper introduces an orthogonal variance-based feature selection method combined with deep neural networks for intrusion detection, achieving perfect accuracy in identifying DDoS attacks.

Contribution

It presents a novel feature selection approach using orthogonal variance decomposition for intrusion detection systems, enhancing detection accuracy.

Findings

Achieved 100% detection accuracy for DDoS attacks.

Demonstrated the effectiveness of the feature selection method.

Showed potential for real-world intrusion detection applications.

Abstract

In this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method.