Orthogonal variance-based feature selection for intrusion detection systems
This addresses the problem of improving intrusion detection systems for network security, but it appears incremental as it combines existing techniques.
The paper tackled the problem of identifying relevant features in network traffic for intrusion detection by applying orthogonal variance decomposition and a deep neural network, achieving 100% detection accuracy for DDoS attacks.
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.