• Prof. Priyanka Kokare
• Prof. Shraddha Mankar
• Prof. Ashvini Nikhade
• Priyanka Tambat

Nowadays, use of Mobile platforms are increasing rapidly. Users are capable of executing increasingly complex tasks very easily by using android apps. Banking is an area where lots of people are doing their banking tasks by using banking apps provided by the respective banks. Mobile devices are targeted by malware’s due to rising use of android apps. Due to openness feature of android it became favorite for users and developers alike.Users are downloading number of apps from Play store any time. During downloading, the number of malware’s incurred with apps, behind the scene; Malware's performs the various activities like hacking the authentication process which contains various confidential data of Users and Login activities. Due to this increasing use of mobile apps for work, Users are losing their Smartphone’s Integrity and Confidentiality. Developers are free to develop any kind of apps and without doing any scrutiny of their apps, developers are publishing their own created apps in a play store. Developers, who are hackers, are taking advantage of user illiteracy about this malware attacks through the apps.This paper, presents the system which would help to provide protection to users android phones by analyzing and removing such malicious apps. The proposed system would work by analyzing the permission’s as features which are taken by users during installation. Clustering and Classification techniques are used to analyze the apps. The main motivation of this paper is how to find and remove malicious apps which are present in user’s android device.

Benign, Clustering, Data Mining, Apps, Classification, Malicious

"Smartphone Malware's Detection using Data Mining and Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.125-128, May-2019, Available :http://www.jetir.org/papers/JETIRBO06021.pdf

"Smartphone Malware's Detection using Data Mining and Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp125-128, May-2019, Available at : http://www.jetir.org/papers/JETIRBO06021.pdf