A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

Abstract

The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy.

High Accuracy Detection of Mobile Malware Using Machine Learning

“…In [6], the authors presented an approach called eRBCM to detect malware. The eRBCM system was designed using the reinforcement learning approach, which utilizes the strength of Monte-Carlo simulations and builds a strong machine learning model to detect complex malware patterns.…”