We present our work on evaluating the usefulness of deep, convolutional neural networks (DNN) for classifying assembly or machine code as malicious or benign. Our results show that a DNN trained on a small dataset showed 95.1% accuracy in program classification. We also show a modified network can achieve 88% accuracy in classifying nine types of malware on a larger dataset, leaving room for future work to address variable length files.


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    Titel :

    Detecting Malicious Assembly with Deep Learning


    Beteiligte:
    Santacroce, M. (Autor:in) / Koranek, Daniel (Autor:in) / Kapp, David (Autor:in) / Ralescu, Anca (Autor:in) / Jha, R. (Autor:in)


    Erscheinungsdatum :

    2018-07-01


    Format / Umfang :

    103816 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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