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    CloudIntell: An intelligent malware detection system

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    Publication date
    2018-09
    Author
    Mirza, Qublai K.A.
    Awan, Irfan U.
    Younas, M.
    Keyword
    Malware analysis; Machine learning; Cloud; Decision tree; Boosting; SVM; Security
    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/)
    Peer-Reviewed
    Yes
    
    Metadata
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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 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.
    URI
    http://hdl.handle.net/10454/13080
    Version
    Accepted Manuscript
    Citation
    Mirza QKA, Awan I and Younas M (2018) CloudIntell: An intelligent malware detection system. Future Generation Computer Systems. 86: 1042-1053.
    Link to publisher’s version
    https://doi.org/10.1016/j.future.2017.07.016
    Type
    Article
    Collections
    Engineering and Informatics Publications

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