CloudIntell: An intelligent malware detection system

Ali Mirza, Qublai Khan ORCID: 0000-0003-3403-2935, Awan, Irfan and Younas, Muhammad (2018) CloudIntell: An intelligent malware detection system. Future Generation Computer Systems, 86. pp. 1042-1053. doi:10.1016/j.future.2017.07.016

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Abstract

Enterprises and individual users heavily rely on the abilities of antiviruses and other security mechanisms. However, the methodologies used by such software are not enough to detect and prevent most of the malicious activities and also consume a huge amount of resources of the host machine for their regular operations. In this paper, we propose a combination of machine learning techniques applied on a rich set of features extracted from a large dataset of benign and malicious files through a bespoke feature extraction tool. We extracted a rich set of features from each file and applied support vector machine, decision tree, and boosting on decision tree to get the highest possible detection rate. We also introduce a cloud-based scalable architecture hosted on Amazon web services to cater the needs of detection methodology. We tested our methodology against different scenarios and generated high achieving results with lowest energy consumption of the host machine.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Malware analysis; Machine learning; Cloud; Decision tree; Boosting; SVM; Security; Malware detection; Portable executable; AWS; REF2021
Related URLs:	Repository
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Susan Turner
Date Deposited:	10 Sep 2018 14:16
Last Modified:	01 Sep 2023 12:44
URI:	https://eprints.glos.ac.uk/id/eprint/5958

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