TV-ADS: A Smarter Attack Detection Scheme Based on Traffic Visualization of Wireless Network Event Cell

Abstract

To protect the increasing cyberspace assets, attack detection systems (ADSs) as well as intrusion detection systems (IDSs) have been equipped in various network environments. Recently, with the development of big data, machine learning, deep learning, neural networks and other artificial intelligence (AI) technologies, more and more ADSs/IDSs based on Artificial Intelligence are presented in academia and industry. Particularly, depending on the outstanding performance and efficiency in recognizing and classifying images, computer vision algorithms have been employed to detect malicious software and malicious traffic. However, we found that in wireless networks, the results vary significantly depending on the mapping methods used to transform the original network traffic data into visual images. Therefore, in this paper, we propose an AI-based attack detection scheme (TV-ADS) by introducing a novel traffic-image mapping method, which segments the sequential network traffic into individual event cells and transforms variant images to a uniform standard size, and design a CNN model to recognize normal and malicious traffics with these visible network event images. Finally, the results of our experiments on the AWID3 dataset demonstrate that our TV-ADS outperforms the existing schemes in terms of accuracy, precision, recall, F1-score and efficiency.