Performance Comparison of Network Intrusion Detection System Based on Different Pre-processing Methods and Deep Neural Network
Gaurav Meena, Babita Dhanwal, Mehul Mahrishi, Kamal Kant Hiran
Abstract
There is a crucial need to have an intelligent and effective intrusion detection system to overcome network intrusion and cyber security attacks. Through this paper, the author compares various data pre-processing methods categorized as Feature selection, Feature encoding, and Feature scaling. The pre-processed data and an Autoencoder are used for further processing to get the best features and use them with a deep neural network for classification. Finally, the paper concludes a comparative analysis of pre-processing methods to determine the best for performing network intrusion detection.
Topics & Concepts
Computer scienceIntrusion detection systemAutoencoderFeature selectionArtificial neural networkArtificial intelligenceFeature (linguistics)Network securityData miningEncoding (memory)Feature extractionData processingPattern recognition (psychology)Machine learningComputer securityDatabaseLinguisticsPhilosophyNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques