Intrusion detection using deep sparse auto-encoder and self-taught learning

With the enormous increase in the use of the Internet, secure transfer of data across networks has become a challenging task. Attackers are in continuous search of getting information from network traffic, and this is the main reason that efficient intrusion detection techniques are required to identify different kinds of network attacks. In past, various supervised and semi-supervised methods have been developed for intrusion detection. Most of these methods require a large amount of data to develop an efficient intrusion detection system. In the proposed deep neural network and adaptive self-taught-based transfer learning technique, we have exploited the concept of self-taught learning to train deep neural networks for reliable network intrusion detection. In the proposed method, a pre-trained network on regression-related task is used to extract features from NSL-KDD dataset. Original features along with extracted features from the pre-trained network are then provided as an input to the sparse auto-encoder. Self-taught learning-based extracted features, when concatenated with the original features of NSL-KDD dataset, enhance the performance of the sparse auto-encoder. Performance of self-taught learning-based approach is compared against several techniques using ten independent runs in terms of accuracy, false alarm and detection rate, area under the ROC, and PR curve. It is experimentally observed that the auto-encoder trained on the combined original and extracted features is stable and offers good generalization in comparison with the sparse auto-encoder trained on original features alone.