Yes / 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 oper-
ations. 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 les through a bespoke feature extraction tool. We extracted a rich
set of features from each le 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
di erent scenarios and generated high achieving results with lowest energy con-
sumption of the host machine.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/13080 |
| Date | 25 July 2017 |
| Creators | Mirza, Qublai K.A., Awan, Irfan U., Younas, M. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted Manuscript |
| Rights | © 2018 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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