1
A Survey on Adversarial Attacks
for Malware Analysis
Kshitiz Aryal, Maanak Gupta, Member, IEEE, and Mahmoud Abdelsalam, Member, IEEE
Abstract—The last decade has experienced an exponential growth in research and adoption of Machine Learning (ML) and Artificial
Intelligence (AI), and its application in different use-cases. Traditional machine learning algorithms have evolved into data intensive deep
learning architectures, which have fostered cutting edge and unprecedented technological advancements revolutionizing today’s world.
The capability of these ML algorithms to uncover knowledge and patterns from semi- or unstructured data to support automation in
decision making has led to the revamping of domains such as medicine, e-commerce, autonomous cars, and cybersecurity. The growth
and adoption of machine learning solutions have been lately slowed down with the advent of adversarial attacks. Adversaries are able
to modify data at training and testing time, maximizing the classification error of the ML models. Minor intentional perturbations in test
samples are crafted by the adversaries to exploit the discovered blind spots in trained models. The increased data dependency of these
algorithms have offered a way for high incentives to disguise ML models. In order to survive against possible catastrophic implications,
continuous research is required to find vulnerabilities in form of adversarial and design resilient autonomous systems.
Machine learning-based malware analysis approaches are widely researched and deployed in critical infrastructures for detecting
and classifying evasive and growing malware threats. However, minor perturbations or few ineffectual bytes insertion can easily ’fool’
these trained ML classifiers, essentially making them ineffective against these crafted and smart malicious software. This survey aims
at providing an encyclopedic introduction to adversarial evasion attacks that are carried out specifically against malware detection and
classification systems. Since most of the research in the adversarial malware domain is new and has been performed in the last couple
of years, our survey will cover the relevant literature published on the malware adversarial evasion attacks between the year 2013 to
2021. The paper will begin by introducing various machine learning techniques used to generate adversarial in malware analysis and
explaining the structures of target files. The survey will model the threat posed by adversaries followed by brief descriptions of widely
used adversarial algorithms. We will also provide a taxonomy of adversarial evasion attacks with respect to the attack domains and
adversarial generation techniques that are widely used in malware detection and classification. Adversarial evasion attacks carried out
against malware detectors will be discussed under each taxonomical headings and compared with related literature. The survey will
conclude by highlighting the open problems, challenges and future research directions.
Index Terms—Adversarial Evasion attack, Adversary Modeling, Data Poisoning, Malware Analysis, Machine Learning, Deep Learning,
Security, Windows Malware, Android Malware, PDF Malware
F
1 INTRODUCTION
M
ACHINE Learning has revolutionized the modern
world due to its ubiquity and generalization power
over the humongous volume of data. With Zettabytes of
data hovering around the cloud [1], modern technology’s
power resides in extracting knowledge from these un-
structured raw data. Machine learning (ML) has provided
the unprecedented power to automate the decision-making
process, outperforming humans by far margin. ML has
powered more robust and representative feature set in com-
parison to hand-crafted features. Transformation of ML ap-
proaches from classical algorithms to modern deep learning
technologies are providing the major breakthroughs in state-
of-art research problems. Further, deep learning (DL) has
excelled in areas where traditional ML approaches were
• Kshitiz Aryal is a Doctoral student in the Department of Computer
Science, Tennessee Tech University, Cookeville, TN, 38501, USA.
E-mail: karyal42@tntech.edu
• Maanak Gupta is an Assistant Professor in the Department of Computer
Science, Tennessee Tech University, Cookeville, TN, 38501, USA.
E-mail: mgupta@tntech.edu
• Mahmoud Abdelsalam is an Assistant Professor in the Department of
Computer Science, North Carolina A&T State University, NC, USA.
E-mail: mabdelsalam1@ncat.edu
infeasible (or unsuccessful) to apply. The evolving deep
learning techniques have furnished the fields of natural
language processing [2], [3], [4], image classification [5],
[6], [7], [8], autonomous driving [9], [10], [11], [12], neuro
science [13], [14], [15], [16] and many other wide range of do-
mains. Society is experiencing high-end amazing products
like Apple Siri
1
, Amazon Alexa
2
and Microsoft Cortana
3
due to recent advances in machine learning and artificial
intelligence (AI). Needless to say that machine learning has
started shaping our daily life habits; connecting to people on
social media, ordering food and groceries from online stores,
listening to music on Spotify
4
, watching movies on Netflix
5
,
reading online news and books, are all examples of systems
built around the recommendation engines powered by deep
learning based models. Machine learning based solutions
not only control our lifestyle but it has also revolution-
ized cyber security critical operations in different domains
including malware analysis [17], [18], [19], [20], [21], [22],
spam filtering [23], [24], [25], [26], fraud detection [27], [28],
1. https://www.apple.com/siri/
2. https://alexa.amazon.com/
3. https://www.microsoft.com/en-us/cortana
4. https://www.spotify.com/us/
5. https://www.netflix.com/
arXiv:2111.08223v2 [cs.CR] 5 Jan 2022
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