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Adversarial Attacks and Defenses in Malware Classification: A Survey
Ilja Moisejevs
Pages - 31 - 43     |    Revised - 31-08-2019     |    Published - 01-10-2019
Volume - 8   Issue - 3    |    Publication Date - October 2019  Table of Contents
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KEYWORDS
Machine Learning, Malware Classification, Adversarial Attacks, Evasion Attacks.
ABSTRACT
As malware continues to grow more sophisticated and more plentiful - traditional signature and heuristics-based defenses no longer cut it. Instead, the industry has recently turned to using machine learning for malicious file detection. The challenge with this approach is that machine learning itself comes with vulnerabilities - and if left unattended presents a new attack surface for attackers to exploit.

In this paper we present a survey of research in the area of machine learning-based malware classifiers, the attacks they encounter, and the defensive measures available. We start by reviewing recent advances in malware classification, including the most important works using deep learning. We then discuss in detail the field of adversarial machine learning and conduct an exhaustive review of adversarial attacks and defenses in the field of malware classification.
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Mr. Ilja Moisejevs
Calypso AI - United Kingdom
umba3abp@gmail.com


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