Autoencoder – Support Vector Machine – Grasshopper Optimization for Intrusion Detection System

Abstract

An intrusion detection system monitors the networks and identifies the malware or suspicious activity in the network. Machine learning techniques were applied in the Intrusion detection system to improve its efficiency in the identifications. Imbalance data problem in intrusion detection affects the performance of identification and deep learning methods have overfitting problems. The autoencoder – support vector machine – grasshopper optimization (AE-SVM-GO) model is proposed to overcome the limitation of the overfitting problem in intrusion detection. The hybrid technique of AE-SVM-GO is applied to solve imbalance data problem and overfitting problem in intrusion detection. The autoencoder model is applied to generate the instances of minority classes to balance the dataset. The Grasshopper optimization performance hyper-parameter optimization in the SVM model to learn the features to adaptively set the parameter in classification. Four datasets such as UNSW-NB15, CICIDS2017, NSL-KDD, and Kyoto 2006+ dataset were used to test the proposed AE-SVM-GO model. The proposed AE-SVM-GO model has 95.3 % accuracy, whereas the existing convolutional recurrent neural network (CRNN) and SVM-naïve bayes model has 76.82 %, and 93.75 % accuracy respectively