Sparse auto encoder driven support vector regression based deep learning model for predicting network intrusions

Abstract

The Network Intrusion Detection System (NIDS) assumes a prominent aspect in ensuring network security. It serves better than traditional network security mechanisms, such as firewall systems. The result of the NIDS indicates the enhanced and efficient performance of the algorithms. It is utilized to predict intrusions, and it also has better training times for the algorithms. In this paper, a capable deep learning model using Sparse Auto Encoder (SAE) is proposed. It is a self-taught learning framework. Such a model is a competent unsupervised learning algorithm in reconstructing new feature representation; thus, it diminishes the dimensionality. The SAE requires minimum training time substantially and efficiently enhances the prediction accuracy of Support Vector Regression (SVR) related to attacks. The experiments are administered using the standard intrusion detection dataset NSL-KDD, and therefore, the implementations are performed using python and tensor flow. The proposed model’s effectiveness is estimated with other models viz., the PCA-SVR and SVR models applying prediction metrics such as R2 score, Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), accuracy and also training time. Results validate that the proposed SAE-SVR model has accelerated the training time of SVR and has the edge over the other models weighed in terms of prediction metrics. The model improves the rate of prediction by bringing down the error rates and yields a pioneering research mechanism for predicting the intrusions.