Remora based Deep Maxout Network model for network intrusion detection using Convolutional Neural Network features

Abstract

With the rapid evolution of the Internet of Things (IoT), network advancement has significantly influenced the increasing number of devices and advanced enhancements linked with it. Indeed the increasing number prevalence and sophistication of emerging cyber-attacks have highlighted the necessity for designing robust security application. In this paper, the Remora-based Deep Maxout Network model is Proposed. Here, the input data is acquired and forwarded to the pre-processing phase, wherein the missing value imputation approach is employed for creating a complete dataset. Later, the pre-processed data is then subjected to dimension transformation from the transformed data; the Convolutional Neural Network features are extracted, followed by feature selection based on Canberra distance. Here, detection is carried out using a Deep Maxout Network whose weights and training parameters are modified using the Remora Optimization Algorithm. However, the proposed model has delivered superior results with a high testing accuracy of 0.945