Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
Demetrio, L
;Biggio, B;Roli, F
2022-01-01
Abstract
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different contexts. We discuss how to develop automated and scalable security evaluations of machine learning using practical attacks, reporting a use case on Windows malware detection.File | Dimensione | Formato | |
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Practical_Attacks_on_Machine_Learning_A_Case_Study_on_Adversarial_Windows_Malware.pdf Solo gestori archivio
Tipologia: versione editoriale
Dimensione 1.37 MB
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1.37 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
2207.05548.pdf accesso aperto
Tipologia: versione post-print
Dimensione 2.6 MB
Formato Adobe PDF
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2.6 MB | Adobe PDF | Visualizza/Apri |
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