We have previously created successful neural networks for malware detection. Here, we examine a network with salience to extract parts of an input deemed important. We show that the blocks we extract are what is important to the network, are unique to their class, and show clear similarities when clustered.


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    Titel :

    A Foray Into Extracting Malicious Features from Executable Code with Neural Network Salience


    Beteiligte:
    Santacroce, Michael (Autor:in) / Stegner, Wayne (Autor:in) / Koranek, Daniel (Autor:in) / Jha, Rashmi (Autor:in)


    Erscheinungsdatum :

    01.07.2019


    Format / Umfang :

    1164456 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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