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@article{145767,
        author = {SASULA RAGHAVENDRA and S. Ramesh},
        title = {Detecting Malware in Google Play by Using Data Mining},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {11},
        pages = {171-174},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145767},
        abstract = {The industrial success of android app markets like Google Play and also the incentive model they provide to well-liked apps, build them appealing targets for false and malicious behaviors. Some dishonest  developers deceivingly boost the search rank and recognition of their apps (e.g., through faux reviews and phoney installation counts), whereas malicious developers use app markets as a launch pad for his or her malware. Proliferation. To spot malware, previous work has targeted on app possible and permission analysis. During this paper, we tend to introduce FairPlay, a unique system that discovers and leverages traces left behind by fraudsters, to sight each malware and apps subjected to go looking rank fraud. FairPlay correlates review activities and unambiguously combines detected review relations with linguistic and behavioural signals gleaned from Google Play app knowledge, so as to spot suspicious apps. FairPlay achieves over ninety fifth accuracy in classifying gold normal datasets of malware, dishonest  and bonafide apps.},
        keywords = {Google play, fraudsters, Fairplay, malware},
        month = {},
        }