MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWARE

International Journal of Network Security & Its Applications (IJNSA) Vol. 10, No.4, July 2018
DOI: 10.5121/ijnsa.2018.10401 1
MINING PATTERNS OF SEQUENTIAL MALICIOUS
APIS TO DETECT MALWARE
Abdurrahman Pektaş1
, Elif Nurdan Pektaş2
and Tankut Acarman1
1
Department of Computer Engineering, Galatasaray University, İstanbul, Turkey
2
Siemens Turkey, Yakack Caddesi No: 111, 34870 Kartal, Istanbul, Turkey
ABSTRACT
In the era of information technology and connected world, detecting malware has been a major security
concern for individuals, companies and even for states. The New generation of malware samples upgraded
with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API
call analysis is used to identify suspicious malicious behavior thanks to its description capability of a
software functionality. In this paper, we propose an effective and efficient malware detection method that
uses sequential pattern mining algorithm to discover representative and discriminative API call patterns.
Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental
results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152
malware samples belonging to 16 families and 523 benign samples.
KEYWORDS
Android, Malware, Frequent Sequence Mining, Behavioural Pattern, API Calls, Dynamic Analysis
1. INTRODUCTION
A malware is deployed to execute malicious behaviours on the compromised system without
authorization and knowledge of its user. A malware steals sensitive information, damages the
integrity of the compromised system, and joins the compromised computer into a part of a cyber-
crime as a boot and so on. A large variety of malware samples exists while targeting different
information systems such as mobile phones (especially Android and IOS), IoT, MAC OS, Linux,
etc. In this study, we focus on Windows based-malware since Microsoft OS is the far more the
most popular desktop OS with a market share of about 82.86% [1].
Concerning security threats and trends, according to 2016 Symantec’s Internet security report [2],
18.4 million mobile malware variants were detected. 4 new mobile families and clusters of 61
distinct new mobile threats were discovered in 2016 versus the identification of 18 new families
and 75 clusters in 2015. The high number of malware samples versus change in growth rate and
new family number can be interpreted such that malware writers have been focused on refining
and modifying existing malware families in order to mitigate detection and execute malicious
intents instead of deploying new and unique malware threats.
There are mainly two reasons about this volume. Firstly, the malware authors can easily access
source code of malware in the Internet and build new cyber weapons. Secondly, malware writers
make use of the runtime packer and obfuscation techniques, which easily enable them creation of
behaviourally identical but statically different malware samples (called malware variants) [3]. To
detect a malware, features are extracted by either static or dynamic analysis. Static features such
as Application Programming Interface (API) calls, opcode sequences, permission requests,
control flows or data flows are extracted from the static source code [4]. These features heavily

2
depend on data distribution and a large dataset enables a comprehensive learning while
introducing more promising detection performance. Dynamic features are extracted by execution
of a given application and activities related to network, file system access and interaction with the
system are monitored [5].
API calls invoked during execution of a program present malicious behaviours and functionality.
Sequential pattern mining and frequent sequence mining is the technique used to discover
meaningful knowledge in the sequential dataset. Sequential pattern mining has been applied
successfully in a variety of data analysis requirements such as bioinformatics, text analysis and
user modelling through mining web usage [6, 7]. In [8], frequent API names and their arguments
are used to represent the behaviour of a malware. To capture the API calls, a malware is run in a
sandbox environment. After getting API calls, frequent item set mining is applied. At the final
stage, the malware dataset is classified according to their families. Based on the experiment
result, 3131 malware samples belonging to 24 malware families are classified with 94.7% F-
measure. But this study does not take into account the sequential nature of the API calls and
particular focus has been on the frequent API calls.
In [9] to capture the common API sequence among different malware categories, the longest
common sequence (LCS) algorithm is used. The captured sequences are treated as the signature.
Similar to the previous work, [10] uses multiple sequence alignment (MSA) algorithms to extract
representative API call patterns of malware families. API call sequences are extracted through a
dynamic analysis approach. After applying MSA to the API calls, the similarity calculation is
performed to classify malware into their respective families.
The main limitation of these methods is that extraction of the longest common API sequence is a
an NP-hard problem [11, 12]. In other words, the time complexity of these methods is
exponentially increasing versus the size of the dataset. [13] introduces a malware detection
approach based on the malicious instruction patterns. The work includes three major steps: 1)
extraction of instruction sequence via disassembling samples, 2) mining malicious instruction
patterns using Generalized Sequential Pattern (GSP) algorithm, and 3) classification of malware
families using the All-Nearest- Neighbour algorithm. A recent study [14] employs the deep neural
network to acquire the representative and distinguishing API call patterns of malware families.
Again, the dynamic analysis method is used to collect API calls. LSTM neural network is applied
to the collected API calls from 787 malware samples belonging to 9 families. The classification
accuracy is reached at 71% on average.
In [15], Canfora et al. presents a mobile malware detection approach based on Hidden Markov
Model and structural entropy. The proposed approach was evaluated on a balanced dataset
including 5560 malware and 5560 trusted Android applications. The experimental results reached
at 98% precision. In [16], Salehi et al employ dynamic feature collected by running malicious
executable into controlled environment. Essentially, the authors use API calls, their arguments
and return values. After applying feature selection on the entire feature set, selected features
achieved 98.1% f-measure on the dataset which consists of 3009 malicious and 1359 benign PE
files. [17] integrates static and dynamic features to identify malware samples. SVM algorithm
achieved more accurate results than Random Forest with 98.7% accuracy. The authors also show
that the combination of static and dynamic features provides the more accurate classification
results.
In this paper we address the challenge of identifying the representative sequence of API calls that
is used by malware authors. We mine API call sequence using sequential pattern mining
approaches and extract meaningful and distinguishing API sub-sequence. Then we use these API
calls to model a malware and apply machine learning algorithms to classify malware samples into

3
their families. According to the experimental results conducted on a fairly large scale and publicly
available dataset including 8,675 samples, the proposed method provides a promising solution to
automatic classification of malware samples.
The remaining of the paper is organized as follows: the proposed method to detect malicious
software by mining frequent sequential APIs is presented in Section 2. Section 3 introduces the
dataset, experimental study and evaluation result. Finally, some conclusions are presented in
Section 4.
2. METHODOLOGY
An overview of the system including its main functionalities is presented in Figure-1. The first
stage is dynamic analysis, more precisely the behaviour of the sample application under analysis
is observed and its reports its interaction with system resources. At this stage, the sample
application is run in a sandboxed environment; Cuckoo [18] to monitor and collect behaviour of a
given executable file. At the second stage, feature to the dynamic dynamic analysis reports. Then,
each malware is modelled by employing the discovered sequence of API calls and each sample is
labeled by using Virustotal [19]. At the final stage, three machine learning algorithms are
evaluated by using a 10-fold cross-validation approach [20].
Figure 1. Overview of the proposed methodology
2.1. DYNAMIC ANALYSIS
API calls must be executed by malware authors in order to perform malicious activities and
intents. In consequence, the behaviour of a sample file can be identified by malware analyst. In
general terms, the behaviour of an executable file can be derived from the sequence of Windows
API calls. System, registry, services and network activities constitute the main functional
categories of the Windows API function calls. During dynamic analysis API calls are encoded
into two length long codes (i.e., kind of words). Successive API calls are ignored during the
encoding procedure. And the semantics of the API calls, which are resilient to obfuscation
techniques, are captured by the encoding method in an effective manner.
2.2.MINING API CALLS
In Windows API, there are different versions for the same API call [21]. For example,
ShellExecute and ShellExecuteEx are two different API functions. Once Windows upgrade an
API, Ex suffix is added to the legacy function names and Microsoft continues to support the old
APIs. Moreover, Microsoft generally uses two distinct API calls for ANSI and Unicode strings. A
suffix is used for ANSI text as input and output, and the W suffix is used Unicode text. For
example, SetWindowTextA and SetWindowTextW are supported for ANSI and Unicode strings,
respectively. To normalize API calls suffix from API calls are removed.
After getting API call sequence from the dataset, we explore the frequent sequence of API calls to
build feature set and then model our dataset. Essentially, sequential API mining reveals
distinctive and representative subsequence in the set of API call sequence. Frequent sequences
can be discovered by different approaches that rely on different measures such as length,
Dynamic
Analysis
API Mining
Classiﬁcation
Model
Evaluation

4
frequency, and support. Generally, the support measure is preferred. The support of a sequence
is formally defined as the number of sequence in which appears and denoted by .
Sometimes the notation relative support is used. Relative support is defined as the ratio of the
number of sequence containing to the total number of sequences. Sequential pattern mining
aims to find all frequent subsequences in the dataset [22, 23]. And the sequence is frequent
sequence if and only if support of is greater than the threshold minimum support value(
) given by the user, .
Discovering frequent sequences in a large-scale dataset is a challenging problem [24]. Various
algorithms such as GSP [25] Spam [26] and Spade [27] have been proposed to efficiently explore
the subsequence. One thing to note that, these algorithms take a sequence dataset and a minimum
support (threshold value) and generate the frequent sequences. The frequent sequence discovery
algorithms generate the same set of subsequence’s. However, these algorithms differ in searching
and navigating the sequential patterns and in utilizing the different data structures.
In our study, we use the SPMF data mining library, an open-source Java library includes more
than 138 implementations of pattern discovery algorithms [28] (SPMF library is available at
[29]). According to the experiment in [28], CM-SPAM algorithm is evaluated as the most
successful sequential pattern mining algorithm in SPMF. Therefore, we choose CM-SPAM
algorithm to discover sequential API patterns. Interested reader can refer to [30] for the CM-
SPAM algorithm, which is evaluated to be faster than SPAM. We tune the following optional
parameters in CM-SPAM implementation:
 Relative support: The ratio of the number of sequence containing si to the total number
of sequences. This parameter is set to 50%.  
 Minimum pattern length: This parameter specifies the minimum length of the API
sequence. It is set to 4.  
 Maximum pattern length: This parameter specifies the maximum length of the API
sequence. It is set to 8.  
 Max gap: This parameter is used to specify if gaps are allowed in sequential patterns. We
set it to 1, which means no gap between each API calls.  
2.3.BUILDING CLASSIFICATION MODEL
A supervised machine learning approach is adopted to classify malware samples like [31]. In the
study, malicious API patterns generated in the previous step are considered as a feature to model
and represent malware samples. We assign a weight to each API pattern by multiplying the
inverse document frequency (idf) and term frequency (tf). The API features are then transformed
into vector representation and each sample is characterized by its feature vector. Namely, each
API call sequence is represented as the vector along with the product of idf and tf. Three well-
known machine classification algorithms including Random Forest [32], K-NN [33] and SVM
[34] are evaluated.
 
3. EVALUATION
We download the latest malware dataset from Virusshare and select the Windows executable files
from the dataset. The malware dataset only includes exe files not dll or Windows installation
file(msi). To identify and verify the Windows exe files, we utilize file command in linux. File
command is aim to determine type of a given file by analyzing its content especially file’s magic
numbers. Magic number also known as file signature is the very first bunch of bytes in a file,
which can uniquely specify the type of a file. Overall, we select 8152 number of malware

5
samples. For the benign dataset, we utilize the exe files located under the C:/ directory of the
fresh installation of the Windows 10 OS. The distribution of the dataset is given in Table-1, in
which P denotes Precision, R denotes Recall and F1 denotes F1-measure statistical metric values,
respectively.
To assess the effectiveness of the selected classifiers, we used four metrics: precision, recall
(a.k.a. sensitivity), F1-score, overall accuracy. We conduct experiments using 10-fold cross-
validation method. In this way, we prevent the over-fitting problem. All experiments are run on a
regular PC environment with a 2-Core i-7 processor and 8 GB memory, using Scikit learn
machine learning library [35]. Table-1 shows the recall, precision and F1-score of each machine
learning algorithm when performing malware family classification. According to the numerical
results related to each metric, Random Forest outperforms the other two classifiers and achieves
the highest precision, recall and F-score for each family. The RF classifiers almost perfectly
classify all families except Virut and Sality families. The main reason behind this is that these two
malware families are very close to each other in terms of executing actions. Besides that, these
two malware families belong to virus malware fam ily.
Table 1. Classification accuracies of the machine learning algorithms per malware families.
We also evaluate the effectiveness of each algorithm using the area under the receiver operating
characteristic (ROC) curve, i.e. AUC. ROC curve is a plot of the TP rate against the FP rate for
different decision thresholds [36]. The area under the ROC curve (AUC) measures the prediction
capability of a binary classifier to differentiate between normal and abnormal classes. The ROC
curve for the tested algorithms is plotted in Figure 2. AUC score 1 means a perfect classification
whereas 0.5 value implies an insignificant result. ROC curves are generally used in the binary
classification problem, to adapt ROC to the multi-classification problem we use macro-averaging
method, which assigns equivalent weight to each class [37]. The best macro-averaged AUC is
achieved by the RF classifier with 0.99 while KNN and SVM are reached at 0.95 and 0.97,
respectively.

6
Figure2. Macro-averaged ROC curves for tested ML algorithms.
In order to statistically compare two classifiers, 10-fold cross-validated paired t-test approach [38]
is applied. When compared with RF and SVM algorithms using paired t-test, we conclude that RF
performs significantly better than SVM since t-value (t = 3.275) is greater than p-value (p =
0.010). However, after applying paired t-test to RF and KNN, two classifiers perform equally
well, since the p-value (p = 0.001) is smaller than the significance level of α = 0.005.
4. CONCLUSIONS
In this paper, we propose a novel sequential API pattern-based malware detection method. To this
end, we dynamically analyze malware samples to extract API calls and then, we use sequential
pattern mining algorithm to find API call patterns. Finally, the three machine learning algorithms
are applied on API call patterns’ features. Random Forest outperforms the other tested algorithms
and achieved 99% F-measure. Experimental results on a fairly large-scale dataset show that the
proposed approach reliably captures malicious representative and distinctive API call patterns and
based on these patterns it can detect malware in an efficient and effective manner. Our proposed
API call pattern-based malware detection approach is usable by many cyber-security tools to
mitigate malware threats. Testing malware detection system with an extended the dataset is
underway.
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AUTHORS
Abdurrahman Pektaş received his B.Sc. and M Sc. at Galatasaray University
and his PhD at the University of Joseph Fourier, all in computer engineering, in
2009, 2012 and 2015, respectively. He is a senior researcher at Galatasaray
University. His research interests are analysis, detection and classification of
malicious software, machine learning and security analysis tool development.
Elif Nurdan Pektaş received his B.Sc. and M Sc. at Galatasaray University all in
computer engineering, in 2010, and 2014, respectively. She is leading software
developer at Siemens Turkey. Her research interests are developing IoT based
applications, deep learning, cloud based application and automated testing.
Tankut Acarman received his Ph.D. degree in Electrical and Computer
engineering from the Ohio State University in 2002. He is professor and head of
computer engineering department at Galatasaray University in Istanbul, Turkey.
His research interests lie along all aspects of autonomous systems, intelligent
vehicle technologies and security. He is the co-author of the book entitled
“Autonomous Ground.

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