Paper Info
Title
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables.
Abstract
Machine learning has already been exploited as a useful tool for detecting malicious executable files. Data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, is leveraged to learn models that discriminate between benign and malicious software. However, it has also been shown that machine learning and deep neural networks can be fooled by evasion attacks (also known as adversarial examples), i.e., small changes to the input data that cause misclassification at test time. In this work, we investigate the vulnerability of malware detection methods that use deep networks to learn from raw bytes. We propose a gradient-based attack that is capable of evading a recently-proposed deep network suited to this purpose by only changing few specific bytes at the end of each malware sample, while preserving its intrusive functionality. Promising results show that our adversarial malware binaries evade the targeted network with high probability, even though less than 1% of their bytes are modified.
Year
DOI
Venue
2018
10.23919/EUSIPCO.2018.8553214
European Signal Processing Conference
DocType
Volume
ISSN
Conference
abs/1803.04173
2076-1465
Citations 
PageRank 
References 
8
0.48
12
Authors
7
Name
Order
Citations
PageRank
Bojan Kolosnjaji1302.99
Ambra Demontis21089.25
Battista Biggio3122473.49
Davide Maiorca439720.20
Giorgio Giacinto52196125.33
Claudia Eckert628818.48
Fabio Roli74846311.69