Network intrusion detection using sparse autoencoder with swish-PReLU activation Model

Abstract

In computer networks, the massive amount of data increases the challenges for intrusion detection systems, because of its high dimensionality. To overcome this problem, a four-phase system is developed for intrusion detection based on encoding techniques and deep learning. In the first phase, input data are collected from NSL-KDD, Canadian Institute for Cybersecurity-Intrusion Detection System 2017 (CIC-IDS2017), and Aegean Wi-Fi Intrusion Dataset (AWID). The collected data are converted into the machine-readable form by using label and one hot encoding technique that reduces the human intervention process and increases the accuracy of data classification. Next, the top percentile and recursive features are selected utilizing second percentile methodology and recursive feature elimination. The undertaken feature selection techniques; second percentile method and recursive feature elimination selects the relevant or active features from the pre-processed data that effectively diminishes the computational time and complexity of the proposed model. In the final phase, sparse autoencoder with swish-PReLU activation model is proposed to classify the normal and traffic types in the NSL-KDD, CIC-IDS2017, and AWID datasets. In the experimental phase, the proposed sparse autoencoder with swish-PReLU activation model achieved effective performance in intrusion detection in light of false alarm rate, detection rate and classification accuracy. From the experimental result, the proposed model showed the maximum of 4.77% improvement in classification accuracy compared to the existing models; correlation-based feature selection with bat algorithm, artificial neural network, chi-square and information gain-random tree, and sequential Search-Bayesian network.