Searching Grade Scheme and Malware Recognition Within Google Play Application
DOI:
https://doi.org/10.32628/IJSRST196177Keywords:
Fair Play, Dataset, Google Play, Malware, Google Bouncer, Proliferation.Abstract
To introduce FairPlay, a work of fiction system that discover and leverages traces left behind by fraudsters, to distinguish both malware and apps subjected to investigate status fraud. FairPlay associate review behavior and distinctively combine detect review associations with linguistic and behavioral signals gleaned from Google Play app records (87 K apps, 2.9 M reviews, and 2.4M reviewers, unruffled over half a year), in order to organize suspicious apps. FairPlay achieves over 95 percent accuracy in classify gold regular datasets of malware, counterfeit and legitimate apps. Deceptive behaviors in Google Play, the most trendy Android app market, fuel Search rank abuse and malware proliferation. To make out malware, preceding work has paying attention on app executable and acquiescence analysis. It will show that 75 percent of the acknowledged malware apps engage in hunt rank fraud. FairPlay discover hundreds of fraudulent apps that presently evade Google Bouncer
References
- S. Mlot. (2014, Apr. 8). "Top Android App a Scam, Pulled From Google Play," PCMag. Available: http://www.pcmag.com/article2/0,2817,2456165,00.asp
- D. Roberts. (2015, Jul. 8). "How to spot fake apps on the Google Play store," Fortune. Available: http://fortune.com/2015/07/08/google-play-fake-app/
- I.Burguera, U. Zurutuza, and S. Nadjm-Tehrani, "Crowdroid: Behavior based malware detection system for Android,” in Proc.ACM SPSM, 2011, pp. 15–26
- S. Yerima, S. Sezer, and I. Muttik, “Android Malware detection using parallel machine learning classifiers,” in Proc. NGMAST, Sep. 2014, pp. 37–42.
- Alfonso Munoz, Ignacio Mart ˜ ´?n, Antonio Guzman, Jos ´ e Alberto Hern ´ andez, IEEE Android malware detection from Google Play meta-data: Selection of important features.2015, pages,245-251]Alfonso Munoz, Ignacio Mart ˜ ´?n, Antonio Guzman, Jos ´ e Alberto Hern ´ andez, IEEE Android malware detection from Google Play meta-data: Selection of important features.2015, pages,245-251
- Chia-Mei Chen, Je-Ming Lin, Gu-Hsin Lai,IEEE Detecting Mobile Application Malicious Behaviors Based on Data Flow of Source Code.2014 International Conference on Trustworthy Systems and their Applications pp 95-109
- E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw. Detecting product review spammers using rating behaviors. In Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM ’10, pages 939–948, 2010.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST

This work is licensed under a Creative Commons Attribution 4.0 International License.