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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 12 | Dec-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 993
PERMISSION DRIVEN MALWARE DETECTION
USING MACHINE LEARNING
SanchitGupta
Department of Computer Scienceand Engineering, SRM University, Chennai603203, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – Mobiles are ubiquitous in today’s world of
technology. Android is very popular platform among these.
This pervasiveness of Android makes it vulnerable to a
number of attacks. The primary reason for this is the
negligence and lack of knowledge while installing them. A
number of malwares root up from the various permissions
used by applications. What makes Android even moreproneto
these malware attacks is their ability to download and install
apps from various third party sources too. Same application
with same name might be available on various App Stores,
this is taken as an advantage by the attackers to pack these
apps with viruses and to attack the users. This paper’s focus
will be to study the correlation between the various
permissions of Android applications and to find out an
appropriate Machine Learning algorithm which has higher
accuracy rates. Various classification algorithmsareanalyzed
for the best performance and is used for Malware detection
to help users be cautious and informed beforehand. There
are 398 applications data collected with each having 331
permissions data to be analyzed [1]. This huge dataset enable
for a better prediction and decision making.
Keywords: Android, permissions, classification,machine
learning algorithms, Malware detection.
I. INTRODUCTION
The android market is booming platform in mobile market,
the market growth rate is increasing graduallyand it is now
at 84.7% [2].
Android platform is available on mobile phones, tablets,
Smart TVs, etc. While, it may be advantageous to
have this wide spread availability of applications, it canlead
to security issues. For example, iOS users are allowed to
download and install only from the authorized official App
Store of Apple. On the other hand, android permits and
can run many applications downloaded from a gamut of
source, like Direct link downloads, Unofficial App Stores,
Torrents, Google play store, etc. These varied sources make
it easy to distribute malignant applications. The original
applications are repacked with the malicious payload.
These infected applications run some malicious code. They
may try to seek access to many features and applications
such as contacts book, GPS coordinates, messages, personal
information gallery, etc. Moreover, they may also have
monetized advertisements.
Even though a number of security steps have been taken,
it’s still not possible to eradicate these problems until
and unless users are vigilant and more knowledgeable. A
significant number of these risks and threats can be
minimized by judging the various permissions an
application is seeking for. Any application which is seeking
approval of permissions which a user thinks, might not at
all be needed or if the application due to some reasons
doesn’t look like an authentic application must not be
installed. Various machine learning algorithms are
compared viz., Naïve Bayes, Ada Boost, Multi Class
Classifier, Random Tree, Random Forest and J48. To
perform this classification process, a regimented process is
followed. Firstly, the android manifest file is extracted.
Secondly, the information about permissions an
application requires is drawn out. The various permissions
of the entire set of applications is considered. Thirdly, a
database of all the permissions and the respective category
of the application i.e. whether the application is safe (0) or a
malware (1) is entered. Then a number of machine learning
algorithms are used to study the pattern and to identify
the applications are malware or not based on the set of
permissions it seeks.
II. FRAMEWORK
A number of applications are evaluated using various
MachineLearningtechniques.Also, resultsareobservedfora
split of 75% training data and 25% remaining as the test
data. There are many permission attributed considered in
the evaluation.
A) Malware– Mobilemalwareisa maliciouscodedsoftware
that is custom made to attack mobile phones or
smartphones. They try to exploit the main operating
framework i.e. the operating system (OS). These kind of
attacksare increasingand isa major concernfor theandroid
users rather than the iOS users. The main motive of these
coded malwaresis to extract personalsensitive information
and data of the users. This data might include login
credentials, credit card information, etc.
Plenty of detection techniques have been proposed for
android.They are broadlydividedinto two categories,static
and dynamic according to the execution requirements.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 12 | Dec-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 994
B) Static Analysis – It’s the conventional method for
computers, most likely for smart phones. The static analysis
put forth by Schmidt et al. [3] can be executed on mobiles.
Static analysis’s main focus is to analyse the application
before the execution to judge if the application is harmful or
not [4]. This way of analysis involves a lot of storage spaces
for the opcodes, graphs, etc. Also, to use static analysis,
executablefile has to be unpackedand decoded accordingly.
C) Dynamic Analysis – This way of analysis involves the
analysis of the application by its execution on a virtual
machine or real device processor. This is used for the study
of application’s characteristicsand its behaviour.Thecodeis
tracked in and foraged for during the execution. It is more
convenient to apply the dynamic analysis than the static
analysis. Thisparticular paper does detection of malware in
android using the manifest permissions file that aid in the
dynamic detection of the malware during installation.
III. ANDROID APPLICATION PACKAGE
The basic format of an android application is .apk format.
Thisapk file has withinitselfan Androidmanifest.xmlfile.We
need to use thisfile for permissionsextraction.Thefollowing
is a snapshot of the planned process in brief.
The following figure is an instance of Androidmanifest.XML
[5] file.
Figure Permissions Extraction
A. Feature extraction
For the analysis to be efficient, the features which
are not useful or are same for all the applications can be
remove to reduce the size of the dataset. So the first step is
to extract the permission data set of all the apps under
consideration. In the dataset sheet, an attribute “type” is
used to declare if the application is malware-1 or not-0 [1].
The order for analysis to be done is, collect data of android
apps both malware and genuine. Then prepare an excel
sheet to record the data. If required, convert the Numeric
type to Nominal type for analysis. Convert this file to Weka
ARFF format or CSV format. After removing the
unnecessary feature attributes, only 93 attributes for each
application were to be considered. This increased the
efficiency of the algorithms.
IV. VARIOUSTERMINOLOGIESANDEXPERIMENTAL
OBSERVATION
After all unnecessary features are removed from the
dataset, we start the analysis. Some termstobefamiliarwith
are discussed here for better understanding. For analysis, a
confusion matrix is used. Confusion matrix is also called as
coincidence matrix. This confusion matrix is a tableinwhich
the predicted and the actual values are populated.
Accuracy (ACC): It is the ratio of number of correctly
classified instances to the total number of examined
instances.
True Positive Rate (TPR): Also called as recall. It is the
ratio of positive instances correctly classified to the total
number of actual positive instances.
False Positive Rate (FPR): It is the ratio of negative
instances incorrectly classified as positive to the total
number of negative instances.
True Negative Rate (TNR): It is the ratio of negative
instances correctly classified to the total number of actual
negative instances.
False Negative Rate (FNR): It is the ratio of positive
instances incorrectly classified as negative to the total
number of positive instances.
Precision (P): It is the ratio of predicted positive instances
that were correctly predicted to the total number of false
positive and true positive rate.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 12 | Dec-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 995
Table: Weighted average values of Type 0 and 1 (benign
and malware) for various algorithms for class
Algorithm TPR FPR Precisi
on
Accuracy Time
Taken
Naïve Bayes 0.915 0.085 0.916 91.4573% 0.02 s
Ada Boost 0.917 0.083 0.917 91.7085% 0.12 s
Multi Class
Classifier
0.887 0.113 0.887 88.6935% 0.15 s
Random
Tree
0.920 0.080 0.920 91.9598% 0.01 s
Random
Forest
0.932 0.068 0.932 93.2161% 0.3 s
J48 0.927 0.073 0.927 92.7136% 0.07 s
The respective confusion matrices for each of the
algorithmsis shown below.
Class 0 represents – Benign Application
Class 1 represents – Malware Application
Confusion Matrix by the number of applications on record
are recorder and are as follows:
Naïve Bayes:
Ada Boost:
Multi Class Classifier:
Random Tree:
Random Forest:
J48:
V. CONCLUSION AND FUTURE SCOPE
In this paper permission-based analysis was done for
various android applications with each application having
many number of attributes. Different machine learning
algorithms were performed such as.
In this paper different machine learning algorithmsare used
such as Naïve Bayes, Ada Boost,
Multi Class Classifier, Random Tree, Random Forest and
J48. A number of applications are evaluated to detect
whether the application is infected with malware or not.
Totally 398 applications dataset was collected with each
having 331 attributes. To clean the data, many attributes
which weren’t different or significant in the analysis were
removed from the study. So based on this, it was found that
93 attributesare useful for the study. From the study, it was
found that Random Forest has performed betterthan other
classifiers. So it can be used to find out if an application has
malware or not effectively. The performance of classifiers
can be increased greatly by feature reduction.
New effective feature reduction techniques must be
researched to increase efficiency. Also, some other better
classifier algorithms must be researched.
REFERENCES
[1] Urcuqui, C., & Navarro, A. "Machine learningclassifiersfor
android malware analysis", CommunicationsandComputing
(COLCOM), 2016 IEEE Colombian Conference (pp. 1-6).
[2] Madihah Mohd Saudi and Zul Hilmi Abdullah, "An
Efficient Framework to Build Up Malware Dataset,"
International Journal of Computer, Electrical, Automation,
Control and Information Engineering, vol. 7, pp. 1104-1109,
0ct 7 2013.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 12 | Dec-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 996
[3]A.-D. Schmidt et al, “Static analysis of executables for
collaborative malware detection on android,” IEEE
Conference on Communications, 2009.
[4] Prajakta D. Sawle and Prof. A. B. Gadicha, "Analysis of
Malware Detection Techniques in Android," International
Journal of Computer Science and Mobile Computing, vol. 3,
pp. 176-182, March 2014.
[5] Mohsen Damshenas, Ali Dehghantanha, and Ramlan
Mahmoud, "A survey on malware propagation, analysis and
detection," International Journal of Cyber-Security and
Digital Forensics (IJCSDF), pp. 10-29, 2013.

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Permission Driven Malware Detection using Machine Learning

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 12 | Dec-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 993 PERMISSION DRIVEN MALWARE DETECTION USING MACHINE LEARNING SanchitGupta Department of Computer Scienceand Engineering, SRM University, Chennai603203, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – Mobiles are ubiquitous in today’s world of technology. Android is very popular platform among these. This pervasiveness of Android makes it vulnerable to a number of attacks. The primary reason for this is the negligence and lack of knowledge while installing them. A number of malwares root up from the various permissions used by applications. What makes Android even moreproneto these malware attacks is their ability to download and install apps from various third party sources too. Same application with same name might be available on various App Stores, this is taken as an advantage by the attackers to pack these apps with viruses and to attack the users. This paper’s focus will be to study the correlation between the various permissions of Android applications and to find out an appropriate Machine Learning algorithm which has higher accuracy rates. Various classification algorithmsareanalyzed for the best performance and is used for Malware detection to help users be cautious and informed beforehand. There are 398 applications data collected with each having 331 permissions data to be analyzed [1]. This huge dataset enable for a better prediction and decision making. Keywords: Android, permissions, classification,machine learning algorithms, Malware detection. I. INTRODUCTION The android market is booming platform in mobile market, the market growth rate is increasing graduallyand it is now at 84.7% [2]. Android platform is available on mobile phones, tablets, Smart TVs, etc. While, it may be advantageous to have this wide spread availability of applications, it canlead to security issues. For example, iOS users are allowed to download and install only from the authorized official App Store of Apple. On the other hand, android permits and can run many applications downloaded from a gamut of source, like Direct link downloads, Unofficial App Stores, Torrents, Google play store, etc. These varied sources make it easy to distribute malignant applications. The original applications are repacked with the malicious payload. These infected applications run some malicious code. They may try to seek access to many features and applications such as contacts book, GPS coordinates, messages, personal information gallery, etc. Moreover, they may also have monetized advertisements. Even though a number of security steps have been taken, it’s still not possible to eradicate these problems until and unless users are vigilant and more knowledgeable. A significant number of these risks and threats can be minimized by judging the various permissions an application is seeking for. Any application which is seeking approval of permissions which a user thinks, might not at all be needed or if the application due to some reasons doesn’t look like an authentic application must not be installed. Various machine learning algorithms are compared viz., Naïve Bayes, Ada Boost, Multi Class Classifier, Random Tree, Random Forest and J48. To perform this classification process, a regimented process is followed. Firstly, the android manifest file is extracted. Secondly, the information about permissions an application requires is drawn out. The various permissions of the entire set of applications is considered. Thirdly, a database of all the permissions and the respective category of the application i.e. whether the application is safe (0) or a malware (1) is entered. Then a number of machine learning algorithms are used to study the pattern and to identify the applications are malware or not based on the set of permissions it seeks. II. FRAMEWORK A number of applications are evaluated using various MachineLearningtechniques.Also, resultsareobservedfora split of 75% training data and 25% remaining as the test data. There are many permission attributed considered in the evaluation. A) Malware– Mobilemalwareisa maliciouscodedsoftware that is custom made to attack mobile phones or smartphones. They try to exploit the main operating framework i.e. the operating system (OS). These kind of attacksare increasingand isa major concernfor theandroid users rather than the iOS users. The main motive of these coded malwaresis to extract personalsensitive information and data of the users. This data might include login credentials, credit card information, etc. Plenty of detection techniques have been proposed for android.They are broadlydividedinto two categories,static and dynamic according to the execution requirements.
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 12 | Dec-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 994 B) Static Analysis – It’s the conventional method for computers, most likely for smart phones. The static analysis put forth by Schmidt et al. [3] can be executed on mobiles. Static analysis’s main focus is to analyse the application before the execution to judge if the application is harmful or not [4]. This way of analysis involves a lot of storage spaces for the opcodes, graphs, etc. Also, to use static analysis, executablefile has to be unpackedand decoded accordingly. C) Dynamic Analysis – This way of analysis involves the analysis of the application by its execution on a virtual machine or real device processor. This is used for the study of application’s characteristicsand its behaviour.Thecodeis tracked in and foraged for during the execution. It is more convenient to apply the dynamic analysis than the static analysis. Thisparticular paper does detection of malware in android using the manifest permissions file that aid in the dynamic detection of the malware during installation. III. ANDROID APPLICATION PACKAGE The basic format of an android application is .apk format. Thisapk file has withinitselfan Androidmanifest.xmlfile.We need to use thisfile for permissionsextraction.Thefollowing is a snapshot of the planned process in brief. The following figure is an instance of Androidmanifest.XML [5] file. Figure Permissions Extraction A. Feature extraction For the analysis to be efficient, the features which are not useful or are same for all the applications can be remove to reduce the size of the dataset. So the first step is to extract the permission data set of all the apps under consideration. In the dataset sheet, an attribute “type” is used to declare if the application is malware-1 or not-0 [1]. The order for analysis to be done is, collect data of android apps both malware and genuine. Then prepare an excel sheet to record the data. If required, convert the Numeric type to Nominal type for analysis. Convert this file to Weka ARFF format or CSV format. After removing the unnecessary feature attributes, only 93 attributes for each application were to be considered. This increased the efficiency of the algorithms. IV. VARIOUSTERMINOLOGIESANDEXPERIMENTAL OBSERVATION After all unnecessary features are removed from the dataset, we start the analysis. Some termstobefamiliarwith are discussed here for better understanding. For analysis, a confusion matrix is used. Confusion matrix is also called as coincidence matrix. This confusion matrix is a tableinwhich the predicted and the actual values are populated. Accuracy (ACC): It is the ratio of number of correctly classified instances to the total number of examined instances. True Positive Rate (TPR): Also called as recall. It is the ratio of positive instances correctly classified to the total number of actual positive instances. False Positive Rate (FPR): It is the ratio of negative instances incorrectly classified as positive to the total number of negative instances. True Negative Rate (TNR): It is the ratio of negative instances correctly classified to the total number of actual negative instances. False Negative Rate (FNR): It is the ratio of positive instances incorrectly classified as negative to the total number of positive instances. Precision (P): It is the ratio of predicted positive instances that were correctly predicted to the total number of false positive and true positive rate.
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 12 | Dec-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 995 Table: Weighted average values of Type 0 and 1 (benign and malware) for various algorithms for class Algorithm TPR FPR Precisi on Accuracy Time Taken Naïve Bayes 0.915 0.085 0.916 91.4573% 0.02 s Ada Boost 0.917 0.083 0.917 91.7085% 0.12 s Multi Class Classifier 0.887 0.113 0.887 88.6935% 0.15 s Random Tree 0.920 0.080 0.920 91.9598% 0.01 s Random Forest 0.932 0.068 0.932 93.2161% 0.3 s J48 0.927 0.073 0.927 92.7136% 0.07 s The respective confusion matrices for each of the algorithmsis shown below. Class 0 represents – Benign Application Class 1 represents – Malware Application Confusion Matrix by the number of applications on record are recorder and are as follows: Naïve Bayes: Ada Boost: Multi Class Classifier: Random Tree: Random Forest: J48: V. CONCLUSION AND FUTURE SCOPE In this paper permission-based analysis was done for various android applications with each application having many number of attributes. Different machine learning algorithms were performed such as. In this paper different machine learning algorithmsare used such as Naïve Bayes, Ada Boost, Multi Class Classifier, Random Tree, Random Forest and J48. A number of applications are evaluated to detect whether the application is infected with malware or not. Totally 398 applications dataset was collected with each having 331 attributes. To clean the data, many attributes which weren’t different or significant in the analysis were removed from the study. So based on this, it was found that 93 attributesare useful for the study. From the study, it was found that Random Forest has performed betterthan other classifiers. So it can be used to find out if an application has malware or not effectively. The performance of classifiers can be increased greatly by feature reduction. New effective feature reduction techniques must be researched to increase efficiency. Also, some other better classifier algorithms must be researched. REFERENCES [1] Urcuqui, C., & Navarro, A. "Machine learningclassifiersfor android malware analysis", CommunicationsandComputing (COLCOM), 2016 IEEE Colombian Conference (pp. 1-6). [2] Madihah Mohd Saudi and Zul Hilmi Abdullah, "An Efficient Framework to Build Up Malware Dataset," International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 7, pp. 1104-1109, 0ct 7 2013.
  • 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 12 | Dec-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 996 [3]A.-D. Schmidt et al, “Static analysis of executables for collaborative malware detection on android,” IEEE Conference on Communications, 2009. [4] Prajakta D. Sawle and Prof. A. B. Gadicha, "Analysis of Malware Detection Techniques in Android," International Journal of Computer Science and Mobile Computing, vol. 3, pp. 176-182, March 2014. [5] Mohsen Damshenas, Ali Dehghantanha, and Ramlan Mahmoud, "A survey on malware propagation, analysis and detection," International Journal of Cyber-Security and Digital Forensics (IJCSDF), pp. 10-29, 2013.