A Novel Monte-Carlo Simulation-Based Model for Malware Detection (<i>e</i>RBCM)

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 obvi...

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Autores principales:	Muath Alrammal, Munir Naveed, Georgios Tsaramirsis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	malware detection Monte-Carlo simulation reinforcement learning Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/259af961122248c78541aca345f4dadb
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id	oai:doaj.org-article:259af961122248c78541aca345f4dadb
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spelling	oai:doaj.org-article:259af961122248c78541aca345f4dadb2021-11-25T17:25:36ZA Novel Monte-Carlo Simulation-Based Model for Malware Detection (<i>e</i>RBCM)10.3390/electronics102228812079-9292https://doaj.org/article/259af961122248c78541aca345f4dadb2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2881https://doaj.org/toc/2079-9292The 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 <i>e</i>RBCM 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 <i>e</i>RBCM can identify a variety of malware with immense accuracy.Muath AlrammalMunir NaveedGeorgios TsaramirsisMDPI AGarticlemalware detectionMonte-Carlo simulationreinforcement learningElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2881, p 2881 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	malware detection Monte-Carlo simulation reinforcement learning Electronics TK7800-8360
spellingShingle	malware detection Monte-Carlo simulation reinforcement learning Electronics TK7800-8360 Muath Alrammal Munir Naveed Georgios Tsaramirsis A Novel Monte-Carlo Simulation-Based Model for Malware Detection (<i>e</i>RBCM)
description	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 <i>e</i>RBCM 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 <i>e</i>RBCM can identify a variety of malware with immense accuracy.
format	article
author	Muath Alrammal Munir Naveed Georgios Tsaramirsis
author_facet	Muath Alrammal Munir Naveed Georgios Tsaramirsis
author_sort	Muath Alrammal
title	A Novel Monte-Carlo Simulation-Based Model for Malware Detection (<i>e</i>RBCM)
title_short	A Novel Monte-Carlo Simulation-Based Model for Malware Detection (<i>e</i>RBCM)
title_full	A Novel Monte-Carlo Simulation-Based Model for Malware Detection (<i>e</i>RBCM)
title_fullStr	A Novel Monte-Carlo Simulation-Based Model for Malware Detection (<i>e</i>RBCM)
title_full_unstemmed	A Novel Monte-Carlo Simulation-Based Model for Malware Detection (<i>e</i>RBCM)
title_sort	novel monte-carlo simulation-based model for malware detection (<i>e</i>rbcm)
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/259af961122248c78541aca345f4dadb
work_keys_str_mv	AT muathalrammal anovelmontecarlosimulationbasedmodelformalwaredetectionieirbcm AT munirnaveed anovelmontecarlosimulationbasedmodelformalwaredetectionieirbcm AT georgiostsaramirsis anovelmontecarlosimulationbasedmodelformalwaredetectionieirbcm AT muathalrammal novelmontecarlosimulationbasedmodelformalwaredetectionieirbcm AT munirnaveed novelmontecarlosimulationbasedmodelformalwaredetectionieirbcm AT georgiostsaramirsis novelmontecarlosimulationbasedmodelformalwaredetectionieirbcm
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A Novel Monte-Carlo Simulation-Based Model for Malware Detection (<i>e</i>RBCM)

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