Open AccessHGM: A Novel Monte-Carlo Simulations based Model for Malware Detection
Author(s) -
Munir Naveed,
Muath Alrammal,
A. Bensefia
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/946/1/012003
Subject(s) - malware , computer science , monte carlo method , reinforcement learning , artificial intelligence , heuristic , machine learning , task (project management) , sophistication , computer security , mathematics , engineering , statistics , social science , systems engineering , sociology
Malware detection is a challenging and non-trivial task due to ever increase in several attacks and their sophistication level. Detection of such attacks demands the exploration of new approaches to generalize the attack patterns. One such approach is the use of Monte-Carlo simulations to train a reinforcement learning model. In this paper, we propose a self-adaptive Monte-Carlo simulation-based reinforcement model called Heuristic-based Generative Model (HGM), which generalizes the attack patterns in such a way that the new unknown attacks can be detected and flagged in real-time. The results show that HGM can detect a variety of malware with high accuracy.