Orthogonal variance-based feature selection for intrusion detection systems
Firuz Kamalov, Sherif Moussa, Ziad El‐Khatib, Adel Ben Mnaouer
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.
Topics & Concepts
Intrusion detection systemComputer scienceFeature selectionArtificial intelligenceDenial-of-service attackConstruct (python library)Variance (accounting)Data miningPattern recognition (psychology)Artificial neural networkSelection (genetic algorithm)Feature extractionNetwork securityFeature (linguistics)DecompositionMachine learningLinguisticsOperating systemAccountingProgramming languageThe InternetWorld Wide WebPhilosophyEcologyBiologyBusinessNetwork Security and Intrusion DetectionAnomaly Detection Techniques and ApplicationsWireless Signal Modulation Classification