Abstract— analysis is mainly focused on the manifest.xml file

Abstract— Today, the use of mobile phone is growing in all
the areas and unfortunately, it made the mobile phones a
continuous target of cyber attackers. The main source of these
kinds of attack is the malicious applications which a user will be
downloading from trusted mediums such as Playstore, App store
and all. Considering the millions of applications, the play store is
having, it is impossible to identify which one is malicious and
which one is not for a user. Even after the installation, the user
will not be able to understand the activities the application will be
performing in the mobile device. A lot of problems are arising
nowadays because of this and a lot of confidential information is
getting leaked from the mobile device. So, it is important to have
a platform where it should be able to distinguish a malicious app
from the set of benign app.

This system is a mobile android application which will be
working based on machine learning. The application will
perform both static and dynamic analysis to identify the
malicious activities of an application. The static analysis is mainly
focused on the manifest.xml file of an Android application and
the dynamic analysis will be based on the actions it will be
triggering while running on a mobile device. The system can
combine both static and dynamic analysis results. The main aim
of this project is to develop an efficient and effective android
mobile application with a high success rate of distinguishing
malicious from benign applications.

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Keywords—Android, Malicious Apps, Machine Learning


It is always an open war between the attackers and
defenders. The defenders will make use of new technologies to
stop the attackers and the attackers will try their level best to
bypass the wall created by the defenders. For example, when
anti-virus makers came up with signature analysis to protect
the platforms, the attackers started creating new/encrypted
signatures to bypass that. This made the need for a new
technique and that is what we are trying to implement in our

978-1-4673-8855-9/17/$31.00 ©2018 IEEE


Assistant Professor (O.G) I.T. Department
SRM Institute of Science and Technology, Kattankulathur
Chennai, India
[email protected]

system using the Machine Learning Technique considering
Machine Learning is the future.

Today we say Machine Learning as the future. The reason
is that, if you search around, you will understand that there is a
lot of data everywhere. Starting from text messages to
Facebook, email, maps and the list goes on. So, it became very
necessary to manage these data’s in an efficient way. If you
consider humans, there is a limit for data a human can manage.
So, there is one way left and that is the Machines Learning. A
machine learning is the ability of a machine to learn without
being explicitly programmed. It’s like, if you tell the machine
to perform one task 2 times repeatedly, 3rd time the machine
will do it automatically and the 4th time it will do it better than
the previous time. If that good the machine learning is then the
outcome we will be getting once we use this concept for
developing a mobile android application that can be used to
differentiate malicious apps and benign apps should be more
efficient. That is what our aim is and here, we will be
developing a mobile application with the purpose of detecting
and analyzing malicious apps and it will be working on the
basis of Machine Learning.



App Compatibility

One familiar word that we will come across while
developing an android app is the “compatibility” and here it is
the app compatibility. Android apps can run on many devices,
starting from phones, tablets, and television. It is bound to
have some variances in the features based on the devices. So,
it is very important to consider whether your application is
compatible with all the kind of devices. As android can run on
many device configurations and not all features are available
on all the devices. Considering this app will be using machine
learning, it is important to make sure the algorithm will not get
affected because of this variance. 


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