Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection

Abstract

Network intrusion detection systems (NIDSs) provide a better solution to network security than other traditional network defense technologies, such as firewall systems. The success of NIDS is highly dependent on the performance of the algorithms and improvement methods used to increase the classification accuracy and decrease the training and testing times of the algorithms. We propose an effective deep learning approach, self-taught learning (STL)-IDS, based on the STL framework. The proposed approach is used for feature learning and dimensionality reduction. It reduces training and testing time considerably and effectively improves the prediction accuracy of support vector machines (SVM) with regard to attacks. The proposed model is built using the sparse autoencoder mechanism, which is an effective learning algorithm for reconstructing a new feature representation in an unsupervised manner. After the pre-training stage, the new features are fed into the SVM algorithm to improve its detection capability for intrusion and classification accuracy. Moreover, the efficiency of the approach in binary and multiclass classification is studied and compared with that of shallow classification methods, such as J48, naive Bayesian, random forest, and SVM. Results show that our approach has accelerated SVM training and testing times and performed better than most of the previous approaches in terms of performance metrics in binary and multiclass classification. The proposed STL-IDS approach improves network intrusion detection and provides a new research method for intrusion detection.