A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network

A large number of attacks are launched daily in the era of the internet and with a large number of users. Nowadays, effective detection of numerous attacks using the Intrusion Detection System (IDS) is an emerging research technique. Machine learning methodologies show effective results in intrusion detection system. We proposed a two-stage hybrid methodology for intrusion detection. In the first stage, the unsupervised Sparse autoencoder (SAE) with smoothed l1 regularization. We employ smoothed l1 regularization to enforce a sparsity of autoencoder. The smoothed l1 regularization is indeed able to learn sparse representations of features. In the second stage, the Deep Neural Network (DNN) was used to predict and classify attacks. The classifier classifies multi attack classification from the extracted features. Unsupervised SAE was optimized to train an efficient model. The experimental results demonstrate that proposed model better than the conventional models in terms of overall performance in detection rate and low false positive rate. The proposed model was assessed on the datasets KDDCup99, NSL-KDD and UNSW-NB15. The model attained the accuracy 99.98% , and detection rate 99.99% on UNSW-NB15 dataset.