• SHAIK ABDUL JABBAR
• A.EMMANUEL RAJU
• H ATEEQ AHMED

The use of mobile devices including Tablets, Smart watch, and note books are increasing day by day. Android has the major share in the mobile application market. Android mobile applications become an easy target for the attackers because of its open source environment. Also user’s ignorance the process of installing and usage of the apps. To identify fake and malware applications, all the previous methods focused on getting permission from the user and executing that particular mobile application. A malware detection framework that discovers and break traces left behind by fraudulent developers, to detect search rank fraud as well as malware in Google Play. The fraud app is detected by aggregating the three pieces of evidence such as ranking based, co-review based and rating based evidence. Finally aggregating all the activities of front running apps, it can be achieve certain accuracy in classifying benign standard datasets of malware, fraudulent and legitimate apps. Additionally, we apply incremental learning approach to characterize a large number of data sets. It combined effectively for all the evidences for fraud detection. To accurately locate the ranking fraud, there is a need to mining the active period’s namely leading sessions, of mobile Apps.

MALWARE AND FAKE APPLICATIONS IDENTIFY IN GOOGLE PLAY

"MALWARE AND FAKE APPLICATIONS IDENTIFY IN GOOGLE PLAY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.77-84, January-2019, Available :http://www.jetir.org/papers/JETIR1901B12.pdf

"MALWARE AND FAKE APPLICATIONS IDENTIFY IN GOOGLE PLAY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp77-84, January-2019, Available at : http://www.jetir.org/papers/JETIR1901B12.pdf