Malware Attack Detection in Large Scale Networks using the Ensemble Deep Restricted Boltzmann Machine

Abstract

Today, cyber attackers use Artificial Intelligence (AI) to boost the sophistication and scope of their attacks. On the defense side, AI is used to improve defense plans, robustness, flexibility, and efficiency of defense systems by adapting to environmental changes. With the developments in information and communication technologies, various exploits that are changing rapidly constitute a danger sign for cyber security. Cybercriminals use new and sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable, and strong cyber defense systems that can identify a wide range of threats in real time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. This paper presents an Ensemble Deep Restricted Boltzmann Machine (EDRBM) to classify cybersecurity threats in large-scale network environments. EDRBM acts as a classification model that enables the classification of malicious flowsets in a large-scale network. Simulations were carried out to evaluate the efficacy of the proposed EDRBM model under various malware attacks. The results showed that the proposed method achieved a promising malware classification rate in malicious flowsets.