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    Malware Detection by Eating a Whole EXE

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    Links to Files
    https://aaai.org/ocs/index.php/WS/AAAIW18/paper/viewFile/16422/15577
    Permanent Link
    http://hdl.handle.net/11603/12714
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    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
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    Author/Creator
    Raff, Edward
    Barker, Jon
    Sylvester, Jared
    Brandon, Robert
    Catanzaro, Bryan
    Nicholas, Charles
    Type of Work
    9 pages
    Text
    conference papers and proceedings
    Citation of Original Publication
    Edward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, Charles Nicholas, Malware Detection by Eating a Whole EXE, The Workshops of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018,https://aaai.org/ocs/index.php/WS/AAAIW18/paper/viewFile/16422/15577
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    malware detection
    image processing
    virtual machine
    Abstract
    In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community. Building a neural network for such a problem presents a number of interesting challenges that have not occurred in tasks such as image processing or NLP. In particular, we note that detection from raw bytes presents a sequence problem with over two million time steps and a problem where batch normalization appear to hinder the learning process. We present our initial work in building a solution to tackle this problem, which has linear complexity dependence on the sequence length, and allows for interpretable sub-regions of the binary to be identified. In doing so we will discuss the many challenges in building a neural network to process data at this scale, and the methods we used to work around them.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.