Hybrid of Distributed Cumulative Histograms and Classification Model for Attack Detection

Abstract

Traditional security systems are exposed to many various attacks, which represents a major challenge for the spread of the Internet in the future. Innovative techniques have been suggested for detecting attacks using machine learning and deep learning. The significant advantage of deep learning is that it is highly efficient, but it needs a large training time with a lot of data. Therefore, in this paper, we present a new feature reduction strategy based on Distributed Cumulative Histograms (DCH) to distinguish between dataset features to locate the most effective features. Cumulative histograms assess the dataset instance patterns of the applied features to identify the most effective attributes that can significantly impact the classification results. Three different models for detecting attacks using Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) are also proposed. The accuracy test of attack detection using the hybrid model was 98.96% on the UNSW-NP15 dataset. The proposed model is compared with wrapper-based and filter-based Feature Selection (FS) models. The proposed model reduced classification time and increased detection accuracy.