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amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth

Mosquera, Alejandro


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        <foaf:name>Mosquera, Alejandro</foaf:name>
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    <dct:title>amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth</dct:title>
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    <dcat:keyword>Adversarial machine learning</dcat:keyword>
    <dcat:keyword>Malware detection</dcat:keyword>
    <dcat:keyword>MLSEC</dcat:keyword>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-09-28</dct:issued>
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    <dct:description>&lt;p&gt;This paper describes the author&amp;#39;s participation in the 3rd edition of the Machine Learning Security Evasion Competition (MLSEC-2021) sponsored by CUJO AI, VM-Ray, MRG-Effitas, Nvidia and Microsoft. As in the previous year the goal was not only developing measures against adversarial attacks on a pre-defined set of malware samples but also finding ways of bypassing other teams&amp;#39; defenses in a simulated cloud environment. The submitted solutions were ranked second in both defender and attacker tracks.&lt;/p&gt;</dct:description>
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