The Curious Case of Machine Learning in Malware Detection
Sherif Saad
1
, William Briguglio
1
and Haytham Elmiligi
2
1
School of Computer Science, Windsor University, Canada
2
Computing Science Department, Thompson Rivers University, Canada
Keywords:
Malware, Machine Learning, Behaviour Analysis, Adversarial Malware, Online Training, Detector Interpre-
tation.
Abstract:
In this paper, we argue that detecting malware attacks in the wild is a unique challenge for machine learn-
ing techniques. Given the current trend in malware development and the increase of unconventional malware
attacks, we expect that dynamic malware analysis is the future for antimalware detection and prevention sys-
tems. A comprehensive review of machine learning for malware detection is presented. Then, we discuss how
malware detection in the wild present unique challenges for the current state-of-the-art machine learning tech-
niques. We defined three critical problems that limit the success of malware detectors powered by machine
learning in the wild. Next, we discuss possible solutions to these challenges and present the requirements of
next-generation malware detection. Finally, we outline potential research directions in machine learning for
malware detection.
1 INTRODUCTION
Nowadays, computer networks and the Internet have
become the primary tool for spreading and distribut-
ing malware by malware authors. The massive num-
ber of feature-rich programming languages and off-
the-shelf software libraries enable the development of
new sophisticated malware such as botnet, fileless,
k-ary and ransomware. New computing paradigms,
such as cloud computing and the Internet of Things,
expand potential malware infection sites from PC’s to
any electronic device.
To decide if software code is malicious or benign
we could either use static analysis or dynamic analy-
sis. Static analysis techniques do not execute the code
and only examine the code structure and other binary
data properties. Dynamic analysis techniques, on the
other hand, execute the code to observe the execution
behaviors of the code over the network or at endpoint
devices. Some malware detection systems apply only
static or dynamic techniques, and some apply both.
While dynamic malware analysis techniques are not
intended to replace static analysis techniques, recent
unconventional malware attacks (botnet, ransomware,
fileless, etc) and the use of sophisticated evasion tech-
niques to avoid detection have shown the urgent need
of dynamic analysis and the limitations of static anal-
ysis. In our opinion, the use of dynamic and beha-
vioral malware analysis will dominate the next-
generation malware detection systems.
There is a general belief among cybersecurity ex-
perts that antimalware tools and systems powered by
artificial intelligence and machine learning will be the
solution to modern malware attacks. The number of
studies published in the last few years on malware
detection techniques that leverage machine learning
is a distinct evidence of this belief as shown in sec-
tion 2. In the literature, various malware detection
techniques using machine learning are proposed with
excellent detection accuracy. However, malware at-
tacks in the wild continue to grow and manage to
bypass malware detection systems powered by ma-
chinelearning techniques. This because it is difficult
to operate and deploy machine learning for malware
detection in a production environment or the perfor-
mance in a production environment is disturbing (e.g.,
high false positives rate). In fact, there is a signif-
icant difference (a detection gap) between the accu-
racy of malware detection techniques in the literature
and their accuracy in a production environment.
A perfect malware detection system will detect all
types of malicious software and will never consider a
benign software as a malicious one. Cohen provided a
formal proof that creating a perfect malware detection
system is not possible (Cohen, 1987; Cohen, 1989).
Moreover, Chess and White proved that a malware
528
Saad, S., Briguglio, W. and Elmiligi, H.
The Curious Case of Machine Learning in Malware Detection.
DOI: 10.5220/0007470705280535
In Proceedings of the 5th International Conference on Information Systems Security and Privacy (ICISSP 2019), pages 528-535
ISBN: 978-989-758-359-9
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
detector with zero false positives is not possible
(M. Chess and R. White, 2000). Selcuk et al. dis-
cussed the undecidable problems in malware detec-
tion in more details (Selcuk et al., 2017). In light of
this, the high levels of accuracy claimed by commer-
cial malware detection systems and some malware de-
tection studies in literature seems questionable.
In this paper, we briefly review the current state
of the art in malware detection using machine learn-
ing approach. Then, we discuss the importance of
dynamic and behavioral analysis based on emerg-
ing malware threats. Next, the shortcomings of the
current machine learning malware detectors are ex-
plained to indicate their limitations in the wild. Fi-
nally, we discuss the possible solutions to improve
the quality of malware detection systems and point
out potential research directions.
2 LITERATURE REVIEW
In recent years, machine learning algorithms have
been used to design both static and dynamic analy-
sis techniques for malware detection. Hassen et al.
proposed a new technique for malware classification
using static analysis based on control statement shin-
gling (Hassen et al., 2017). In their work, they used
static analysis to classify malware instance into new
or known malware families. They extracted features
from disassembled malicious binaries and used ran-
dom forest algorithm to classify malware using the
extracted features. Using a dataset of 10,260 malware
instances, they reported up to 99.21% accuracy.
Static analysis has been used to study malwares
that infect embedded systems, mobile devices, and
other IoT devices. Naeem et al. proposed a static
analysis technique to detect IoT malware (Naeem
et al., 2018). The proposed technique converts a mal-
ware file to a grayscale image and extracts a set of
visual features from the malware image to train an
SVM classifier that could distinguish between mal-
ware families using visual features. Using a dataset
of 9342 samples that belong to 25 malware families,
they reported 97.4% accuracy. Su et al. proposed a
similar technique to classify IoT malware into mal-
ware families using visual features and image recog-
nition (Su et al., 2018). Their approach is very simi-
lar to the one proposed in (Naeem et al., 2018). They
used a one-class SVM classifier and tested their ap-
proach on IoT malwares that infect Linux-like IoT
systems; they reported 94.0% accuracy for detect-
ing malware and 81.8% accuracy for detecting mal-
ware families. Raff et al. proposed a malware detec-
tion technique using static analysis and deep learning
(Raff et al., 2017). The proposed technique achieved
94.0% detection accuracy.
Several works have been proposed to detect An-
droid malware apps using static analysis techniques.
Sahin et al. proposed an Android malware detection
model that uses app permission to detect malicious
apps (OSahn et al., 2018). They used the permissions
required by the app with a weighted distance function
and KNN and Naive Bayes classifier to detect mali-
cious apps. They reported an accuracy up to 93.27%.
Su and Fung used sensitive functions and app permis-
sions to detect Android malware (Su and Fung, 2016).
They used different machine learning algorithms such
as SVM, decision tree, and KNN to build an android
malware detector. They reported an average accuracy
between 85.0% and 90.0%
Collecting and monitoring all malware behaviors
is a complicated and time-consuming process. For
that reason, several works in the literature focused
on collecting partial dynamic behaviors of the mal-
ware. Lim et al. (Lim et al., 2015) proposed a mal-
ware detection technique by analyzing network traf-
fic generated when the malware communicates with
a malicious C&C server such as in the case of bot-
net or ransomware. The proposed technique extracts
a set of features from network flows to present a flows
sequence. The authors used different sequence align-
ment algorithms to classify malware traffic. They re-
ported an accuracy above 60% when analyzing mal-
ware traffic in a real network environment.
Kilgallon et al. applied machine learning and dy-
namic malware analysis (Kilgallon et al., 2017). The
proposed technique gathers register value information
and API calls made by the monitored malware bi-
naries. The collected information is stored in vec-
tor structures and analyzed using a value set analysis
method. Then, they used a linear similarity metric to
compare unseen malware to known malware binaries.
Their experiment showed that the proposed technique
could detect malware with an accuracy up to 98.0%
Omind and Nathan proposed a behavioral-based
malware detection method using a deep belief net-
work (David and Netanyahu, 2015). The proposed
method collected data about malware behaviors from
a sandbox environment. The collected data is API
calls, registry entries, visited websites, accessed ports,
and IP addresses. Then using a deep neural net-
work of eight layers, it generates malware signatures.
These signatures could be used to train malware de-
tectors. In their experiments, they reported up to
95.3% detection accuracy with a malware detector
utilizing the SVM algorithm.
Yeo et al. proposed a new malware detection
method by monitoring malicious behaviors in net-
The Curious Case of Machine Learning in Malware Detection
529
work traffic (Yeo et al., 2018). They designed 35
features to describe malicious traffic of malware in-
stances. They tested several machine learning al-
gorithms including CNN, MLP, SVM, and random
forest. The proposed method achieved an accuracy
above 85% when utilizing CNN or random forest.
Prokofiev et al. proposed a machine-learning tech-
nique to detect C&C traffic of infected IoT devices
(Prokofiev et al., 2018). The proposed approach used
network traffic features such as port number, IP ad-
dresses, connection duration and frequency. They re-
ported a detection accuracy up to 97.3%. However,
the proposed approach is still relying on traditional
malware analysis methods and will not be able to
work in production IoT deployment as discussed in
(Soliman et al., 2017). Several hybrid malware de-
tection techniques that combine both static and dy-
namic analysis have also been proposed (Martinelli
et al., 2016; Paola et al., 2018). These techniques try
to improve the quality and performance of malware
detection systems by taking advantage of static and
dynamic analysis to build robust malware detection
systems.
3 EMERGING MALWARE
THREATS
With the recent changes in malware development
and the rise of commercial malware (malicious code
rented or purchased), many new challenges are facing
malware analysts that make static analysis more diffi-
cult and impractical. These challenges will force an-
timalware vendors to adapt behavioral malware anal-
ysis and detection techniques. In our opinion, there
are two main reasons behind these challenges; the rise
of unconventional computing paradigms and uncon-
ventional evasion techniques. There is a new gener-
ation of malwares that take advantage of unconven-
tional computing paradigms and off-the-shelf soft-
ware libraries written by feature-rich programming
languages. The current state-of-the-art malware anal-
ysis/detection techniques and tools are not effective
against this new generation of malware.
3.1 Unconventional Computing
Paradigms
New computing paradigms and technologies such as
cloud computing, the internet of things, big data, in-
memory computing, and blockchain introduced new
playgrounds for malware authors to develop com-
plex and sophisticated malwares that are almost un-
detectable. Here we describe several recent examples
of new malware threats that are difficult to detect or
analyze using static analysis.
For instance, the Internet of Things (IoT) is an ap-
pealing platform for modern and sophisticated mal-
ware scuh as ransomware. Zhang-Kennedy et al. dis-
cussed the ransomware threat in IoT and how a self-
spreading ransomware could infect an IoT ecosystem
(Zhang-Kennedy et al., 2018). The authors pointed
out that the ransomware will mainly lock down IoT
devices and disable the essential functions of these
devices. The study focused on identifying the attack
vectors in IoT, the techniques for ransomware self-
spreading in IoT, and predicting the most likely class
of IoT applications to be a target for ransomware at-
tacks. Finally, the authors identified the techniques
the ransomware could apply to lock down IoT de-
vices. Authors in (Zhang-Kennedy et al., 2018) used
a Raspberry to develop a proof of concept IoT ran-
somware that can infect an IoT system. One inter-
esting aspect in (Zhang-Kennedy et al., 2018) is the
need for collaboration or swarming behavior in IoT
ransomware, where the IoT ransomware will spread
as much as possible and then lock down the devices
or lock down the device and then spread.
Miller and Valasek developed a proof-of-concept
for malicious code that infects connected cars and
lockdown key functions (Miller and Valasek, 2015).
For instance, the authors demonstrated the ability for
the malicious code to control the steering wheel of a
vehicle, disable the breaks, lock doors, and shut down
the engine while in motion. Behaving as ransomware,
this real example of a malware that locks and disables
key features in IoT systems (e.g. connected cars)
could have life threatening consequences if the ran-
som is not paid. The study explained a design flow
in the Controller Area Network (CAN) protocol that
allows malicious and crafted CAN messages to be in-
jected into the vehicle CAN channel by a compro-
mised mobile phone that is connected to the vehicle
entertainment unit. It was reported that for some ve-
hicles only the dealership could restore and patch the
vehicle to prevent this attack. Choi et al. proposed a
solution for malware attacks in connected vehicles us-
ing machine learning (Choi et al., 2018). The solution
uses SVM to distinguish between crafted malicious
CAN messages, and benign CAN messages generated
by actual electronic control units (ECU). The model
extracts features from the vehicle ECUs and creates
fingerprints for those ECUs. The ECU fingerprint is
noticeable in a benign CAN message and does not ex-
ist in a malicious message
Azmoodeh et al. discussed a new technique to de-
tect ransomware attacks in IoT systems by monitor-
ICISSP 2019 - 5th International Conference on Information Systems Security and Privacy
530
ing the energy consumption of infected devices (Az-
moodeh et al., 2018). As a proof of concept, they
studied the energy consumption of infected Android
devices. The devices were infected by a ransomware
with crypto impact. They used different machine
learning models (KNN, SVM, NN, and Random For-
est) to analyze energy consumption data and extract
unique patterns to detect compromised Android de-
vices. They reported a ransomware detection accu-
racy of 95.65%.
In 2015, Karam (INTERPOL) and Kamluk
(Kaspersky lab) introduced a proof of concept
distributed malware that also takes advantage of
blockchain technology (Karam and Kamluk, 2015).
In 2018, Moubarak and et al. provided design and
implementation of a K-ary malware (distributed mal-
ware) that takes advantages of the blockchain net-
works such as Etherum and Hyperledger (Moubarak
et al., 2018). The proposed malware is stored and ex-
ecuted inside blockchain networks and acts as a ma-
licious keylogger. While detecting a K-ary malware
is an NP-hard problem(de Dr
´
ezigu
´
e et al., 2006), it
is also complicated to implement a K-ary malware.
However, Mubarak’s works demonstrated the sim-
plicity of K-ary malware development by taking ad-
vantage of blockchain technology as distributed and
decentralized network.
3.2 Unconventional Evasion Techniques
The new generation of malware will use advanced
evasion techniques to avoid detection by antimalware
systems and tools. New evasion techniques imple-
mented by malware authors use new technologies and
off-the-shelf software libraries that enable the design
of sophisticated evasion methods. Antimalware ven-
dors and malware researchers discussed recent exam-
ples of using new antimalware evasion techniques in
the wild.
Fileless malware or memory-resident malware is
the new technique used by malware authors to de-
velop and execute malicious attacks. Fileless mal-
ware resides in device memory and does not leave any
files on the infected device file system. This makes
the detection of the fileless malware using signature-
based detection or static analysis infeasible. In addi-
tion, the fileless malware takes advantage of the utili-
ties and libraries that already exist in the platform of
the infected device to complete its malicious intents.
In other words, benign applications and software li-
braries are manipulated by fileless malware to accom-
plish the attack objectives.
Fileless malware attacks and incidents are already
observed in the wild compromising large enterprises.
According to KASPERSKY lab, 140 enterprises were
attacked in 2017 using fileless malwares (Global Re-
search and Analysis Team, KASPERSKY Lab, 2017).
Ponemon Institute reported that 77% of the attacks
against companies use fileless techniques (Ponemon
Institute, Research Report, 2017). Moreover, there
are several signs that ransomware attacks are going
fileless, as discussed in (Magnusardottir, 2018). Be-
sides these signs, there are other reasons in our opin-
ion that confirms that ransomware and other malware
attacks will be fileless. One main reason is the mov-
ing towards in-memory computing.
In recent years, in-memory computing and in-
memory data stores became the first backbone and
storage technology for many organizations. Many big
data platforms and data grids (Apache Spark, Redis,
HazelCast, etc.) enable storing data in memory for
performance and scalability requirements. Valuable
data and information is stored in memory for a long
time before moving to a persistent data store. In-
Memory ransomware that encrypts in-memory data
(such as recent transactions, financial information,
etc.) present a severe and aggressive attack. This is
because any attempt to reset or report the machine
to remove the ransomware from the device memory
or shutdown the application will result in losing this
valuable data permanently.
The moving towards distributed and decentralized
computing is another reason for the rise of fileless ran-
somware. In distributed and decentralized computing
several nodes and devices are available to store the
in-memory malware, which will increase the life ex-
pectancy of the malware since there will always be a
group of active nodes were the malware could repli-
cate and store itself.
The recent and massive development in machine
learning /artificial intelligence (aka data science) and
a large number of off-the-shelf machine learning li-
braries enable malware authors to develop advanced
evasion techniques.
Rigaki and Garcia proposed the use of deep learn-
ing techniques to create malicious malware sam-
ples that evade detection by mimicking the behav-
iors of benign applications (Rigaki and Garcia, 2018).
In their work, a proof of concept was proposed to
demonstrate how malware authors could cover the
malware C&C traffic. The authors use a Genera-
tive Adversarial Networks (GANs) to enable malware
(e.g., botnet) to mimic the traffic of a legitimate ap-
plication and avoid detection. The study showed that
it is possible to modify the source code of malware
to receive parameters from a GAN to change the be-
haviors of its C&C traffic to mimic the behaviors of
other legitimate network applications, such as Face-
The Curious Case of Machine Learning in Malware Detection
531
book traffic. The enhanced malware samples were
tested against the Stratosphere Linux IPS (slips) sys-
tem, which uses machine learning to detect malicious
traffic. The experiment showed that 63.42% of the
malicious traffic was able to bypass the detection.
A research team from IBM demonstrated the use
of artificial intelligence to engineering malware at-
tacks (Kirat et al., 2018). In their study, the authors
proposed DeepLocker as a proof of concept to show
how next-generation malware could leverage artificial
intelligence. DeepLocker is a malware generation en-
gine that malware author could use to empower tra-
ditional malware samples such as WannaCry with ar-
tificial intelligence. A deep convolutional neural net-
work (CNN) was used to customize a malware attack
by combining a benign application and a malware
sample to generate a hybrid malware that bypasses
detection by exposing (mimicking) benign behaviors.
Besides that, the malware is engineered to unlock its
malicious payload when it reaches a target (endpoint)
with a loose predefined set of attributes. In the study,
those attributes were the biometrics feature of the tar-
get such as facial and voice features. The malware
uses CNN to detect and confirm target identity, and
upon target confirmation, an encryption key is gen-
erated and used by the WannCry malware to encrypt
the files on the target endpoint device. The encryption
key is only generated by matching the voice and the
facial features of the target. This means reverse engi-
neering the malware using static analysis is not useful
to recover the encryption key.
4 PRACTICAL CHALLENGES
The new and emerging malware threats discussed in
section 3 provide strong evidence for the need of
adopting dynamic and behavioral analysis to build
malware detection tools. The use of machine learning
is the most promising technique to implement mal-
ware detectors and tools that apply behavioral analy-
sis as shown in section 2. While the use of machine
learning for malware detection has shown promising
results in both static and dynamic analysis, there are
significant challenges that limit the success of ma-
chine learning based malware detectors in the wild.
4.1 Cost of Training Detectors
The first challenge is the cost of training and updating
malware detectors in production environment. Mal-
ware detection is unlike other domains where ma-
chine learning techniques have been applied success-
fully such as computer vision, natural language pro-
cessing, and e-commerce. Malware instances evolve
and change their behaviors over a short period; some
studies by antimalware vendors reported that a new
malware instance could change its behaviors in less
than 24 hours since it has been released (Gupta et al.,
2009; Allix et al., 2015). This means a frequently
trained machine learning model will become out-
dated. This also means we need to frequently retrain
our malware detectors to be able to detect new and
mutated malware instances. Therefore, adaptability
in machine learning models for malware detection is
a crucial requirement and not just a ancillary capabil-
ity.
Recently, the challenge of adaptability, and scal-
ability of machine learning models for malware de-
tection in the wild has become obvious (Narayanan
et al., 2016). The majority of the work proposed in
the literature have done very little to reduce and op-
timize the feature space to design detectors ready for
early malware detection in a production environment
(Hajmasan et al., 2017). For instance, it is not clear
how the proposed detection methods will scale when
the number of monitored endpoints increase. Un-
like computer vision, natural language processing and
other areas that utilize machine learning, malware in-
stances continue to evolve and change. This mostly
requires retraining machine learning models in pro-
duction, which is an expensive and complicated task.
Therefore, when using machine learning for malware
detection, we need to think differently. New methods
to reduce the cost of retraining malware detectors and
improve the detection quality are urgent.
4.2 Malware Detector Interpretability
Cybersecurity analysts always prefer solutions that
are interpretable and understandable, such as rule-
based or signature-based detection. This is because
of the need to tune and optimize these solutions to
mitigate and control the effect of false positives and
false negatives. Interpreting machine learning mod-
els is a new and open challenge (Shirataki and Ya-
maguchi, 2017). However, it is expected that an in-
terpretable machine learning solution will be domain
specific, for instance, interpretable solutions for ma-
chine learning models in healthcare are different than
solutions in malware detection (Ahmad et al., 2018).
Any malware detector will generate false posi-
tives, and unless malware analysts can understand and
interpret the reason that a benign application wrongly
classified as malicious, they will not accept those
black box malware detectors. To our knowledge, no
work in the literature investigated the interpretability
of machine learning models for malware detection.
ICISSP 2019 - 5th International Conference on Information Systems Security and Privacy
532
4.3 Adversarial Malware
Last but not least, a malware detection system utiliz-
ing machine learning could be defeated (bypassed)
using adversarial malware samples. For instance,
Kolosnjaji et al. showed in (Kolosnjaji et al., 2018)
that by using an intelligent evasion attack they can
defeat the deep learning detection system proposed in
(Raff et al., 2017) by Raff et al. They simply used
their knowledge of how the proposed deep learning
detection system operates and designed a gradient-
based attack as an evasion technique to overcome it.
With adversarial malware, the system detection accu-
racy dropped from 94.0% to almost 50.0%. Machine
learning algorithms are not designed to work with ad-
versarial examples. Grosse et al. demonstrated that
using adversarial malware samples; they could reduce
the detection accuracy of a malware detection sys-
tem that uses static analysis and machine learning to
63.0% (Grosse et al., 2017). They also showed that
adopting anti adversarial machine learning techniques
used in computer vision is not effective in malware
detection. Yang et al. proposed adversarial training as
a solution for adversarial malware (Yang et al., 2017).
They designed a method for adversarial android mal-
ware instances generation. The proposed method re-
quires access to the malware binaries and source code,
besides, it is mainly useful for static malware detec-
tion systems.
5 BRIDGING THE DETECTION
GAP
To overcome the challenges we discussed in section 4,
we propose new solutions to mitigate these challenges
and reduce the gap.
5.1 Disposable Micro Detectors
Current best practices in constructing and build-
ing machine learning models follow a mono-
lithic architecture. In monolithic architecture,
a computationally-expensive single-monolithic (to
build and train) machine learning model is used to
detect malwares. While this architecture or approach
for building machine learning models is successful in
other domains, we believe it is unsuitable for mal-
ware detection given the highly evolving character-
istics of malware instance. We propose a new ap-
proach inspired by microservices architecture. In this
approach, multiple, small, inexpensive, focused ma-
chine learning models are built and orchestrated to
detect malware instances. Each model or detector
is built to detect the behaviors of a specific malware
instance (e.g., Mirai, WannaCry), or at most a sin-
gle malware family (a group of similar malware in-
stances). Also, each model or detector is built us-
ing features that are similar, such as having the same
computational cost, or unique to the specific execu-
tion environment. This is because out of the superset
of features designed to detect malware, it is common
that a subset of these features could be more or less
useful to detect a specific malware instance or fam-
ily. The use of micro (small) and focused detectors
reduce the cost of retraining and deployment in pro-
duction. This is because detectors for new malware
could be trained and added without the need to retrain
existing detectors. In addition, when a malware de-
tector becames outdated as a result of malware evolv-
ing behaviors, the outdated detectors are disposed of
and replaced by new ones. The use of micro-detectors
enables adaptability by design rather than attempting
to change machine learning models and algorithms to
support adaptability.
5.2 Analyst Friendly Interpretation
Adopting sophisticated machine learning techniques
for malware detection in a production environment is
a challenge. This is because most of the time it is not
possible to understand how the machine learning sys-
tems make their malware detection decisions. There-
fore, tuning and maintaining these systems is a chal-
lenge in production and new techniques for malware
analysts to interpret and evaluate the performance of
malware detectors are needed. We propose the use
of evolutionary computation techniques such as ge-
netic algorithms or clonal selection algorithms to gen-
erate an interpretation for black-box machine learn-
ing models such as deep learning. Using evolutionary
computation, we could describe the decisions of mal-
ware detectors using a set of IF-Then rules. The only
information required is the input features the malware
detector uses to make a decision.
The IF-Then rules are useful to explain the behav-
iors that trigger a specific decision (e.g., malicious
or benign) by the malware detector. Cybersecurity
and malware analyst are comfortable working with
IF-Then rules. These rules will help in understand-
ing the decision made by malware detectors, explain
the scope of the detection, and identify potential over
generalization or overfitting that could result in false
positives or false negatives.
It is essential that the IF-Then rules set interpre-
tation of the malware detector to be expressed in raw
malware behaviors and not in machine learning fea-
tures. Machine learning features are most likely un-
The Curious Case of Machine Learning in Malware Detection
533
derstandable by machine learning engineers and ex-
perts. The interpretation should be acceptable to a
malware analyst who does not need to be machine
learning experts.
5.3 Anti Adversarial Malware
To improve the resilience of malware detectors
against adversarial malware, we believe it is essential
to study the effort required by the malware authors
to design an adversarial malware for specific malware
detectors. For example, what technique a malware au-
thor would use to probe and study a malware detector
in production to design a malware that could bypass
this detector.
Measuring the effort to probe detectors and design
adversarial malware under two main settings is essen-
tial. The first setting is black-box, where the malware
authors have minimum knowledge about the malware
detector internal design and the features used by the
machine learning algorithm. The second setting is
white-box, where the malware authors have sufficient
knowledge about the malware detector internal design
and the machine learning algorithm. Training and up-
dating the malware detectors is likely the most effi-
cient solution against adversarial malware. Knowing
the effort needed to evade a malware detector will
help in designing training strategies and policies to
increase the effort required to evade the detectors.
As we mentioned before, Cohen provided a for-
mal proof that creating a perfect malware detection
system is not possible (Cohen, 1987; Cohen, 1989).
We believe that designing a perfect adversarial mal-
ware is not possible. Therefore we expect that using
ensemble-based hybrid machine learning approach
for malware detector will be effective against ad-
versarial malware. It is expected that by creating a
malware detector using an ensemble hybrid machine-
learning approach, the risk of evading detection will
decrease and the effort to design adversarial malware
will increase. A hybrid machine learning model is
when two or more different machine learning algo-
rithms are used to construct the model. In the litera-
ture, adversarial malware samples evade malware de-
tectors that use a single machine learning algorithm
or technique (Yang et al., 2017; Grosse et al., 2017;
Kolosnjaji et al., 2018). In our method, a hybrid ma-
chine learning approach for building a malware de-
tector is an approach to provide a defense-in-depth
model for malware detectors.
6 CONCLUSION
In this paper, we reviewed the current state-of-the-art
in malware detection using machine learning. We dis-
cussed the recent trends in malware development and
emerging malware threats. We argued that behavioral
analysis would dominate the next generation antimal-
ware systems. We discussed the challenges of apply-
ing machine learning to detect malware in the wild
and proposed our thoughts on how we could over-
come these challenges. Machine learning malware
detectors require inexpensive training methods; they
need to be interpretable for the malware analysts and
not only for machine learning experts. Finally, they
need to tolerate adversarial malware by design.
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