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 | Size | Format | |
---|---|---|---|
Practical_Attacks_on_Machine_Learning_A_Case_Study_on_Adversarial_Windows_Malware.pdf Solo gestori archivio
Type: versione editoriale
Size 1.37 MB
Format Adobe PDF
|
1.37 MB | Adobe PDF | & nbsp; View / Open Request a copy |
2207.05548.pdf open access
Type: versione post-print
Size 2.6 MB
Format Adobe PDF
|
2.6 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.