Analyzing Fake Ranks In Google Play

Authors(2) :-M. Kishore, P. Shirisha

The commercial success of Android app markets such as Google Play and the incentive model they offer to popular apps, make them appealing targets for false and malicious behaviors. Some fraudulent developers deceptively boost the search rank and popularity of their apps (e.g., through fake reviews and bogus installation counts), while malicious developers use app markets as a launch pad for their malware. Proliferation. To identify malware, previous work has focused on app executable and permission analysis. In this paper, we introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. FairPlay correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from Google Play app data (87K apps, 2.9M reviews, and 2.4M reviewers, collected over half a year), in order to identify suspicious apps. FairPlay achieves over 95% accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75% of the identified malware apps engage in search rank fraud. FairPlay discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. FairPlay also helped the discovery of more than 1,000 reviews, reported for 193 apps, that reveal a new type of “coercive” review campaign: users are harassed into writing positive reviews, and install and review other apps.

Authors and Affiliations

M. Kishore
Department of MCA, Mother Theresa Institute of Computer Applications, Palamaner, India
P. Shirisha
Department of MCA, Mother Theresa Institute of Computer Applications, Palamaner, India

  1. Google Play. https://play.google.com/.
  2. Ezra Siegel. Fake Reviews in Google Play and Apple App Store. Appentive, 2014.
  3. Zach Miners. Report: Malware-infected Android apps spike in the Google Play store. PCWorld, 2014.
  4. Stephanie Mlot. Top Android App a Scam, Pulled From Google Play. PCMag, 2014.
  5. Daniel Roberts. How to spot fake apps on the Google Play store. Fortune, 2015.
  6. Andy Greenberg. Malware Apps Spoof Android Market To Infect Phones. Forbes Security, 2014.
  7. Freelancer. http://www.freelancer.com.
  8. Fiverr. https://www.fiverr.com/.
  9. BestAppPromotion. www.bestreviewapp.com/.
  10. Gang Wang, Christo Wilson, Xiaohan Zhao, Yibo Zhu, Manish Mohanlal, Haitao Zheng, and Ben Y. Zhao. Serf and Turf: Crowdturfing for Fun and Profit. In Proceedings of ACM WWW. ACM, 2012.
  11. Jon Oberheide and Charlie Miller. Dissecting the Android Bouncer. SummerCon2012, New York, 2012.
  12. VirusTotal - Free Online Virus, Malware and URL Scanner. https: //www.virustotal.com/, Last accessed on May 2015.
  13. Iker Burguera, Urko Zurutuza, and Simin Nadjm-Tehrani. Crowdroid: Behavior-Based Malware Detection System for Android. In Proceedings of ACM SPSM, pages 15-26. ACM, 2011.
  14. Asaf Shabtai, Uri Kanonov, Yuval Elovici, Chanan Glezer, and Yael Weiss. Andromaly: a Behavioral Malware Detection Framework for Android Devices. Intelligent Information Systems, 38(1):161-190, 2012.
  15. Michael Grace, Yajin Zhou, Qiang Zhang, Shihong Zou, and Xuxian Jiang. Riskranker: Scalable and Accurate Zero-day Android Malware Detection. In Proceedings of ACM MobiSys, 2012.
  16. Bhaskar Pratim Sarma, Ninghui Li, Chris Gates, Rahul Potharaju, Cristina Nita-Rotaru, and Ian Molloy. Android Permissions: a Perspective Combining Risks and Benefits. In Proceedings of ACM SACMAT, 2012.
  17. Hao Peng, Chris Gates, Bhaskar Sarma, Ninghui Li, Yuan Qi, Rahul Potharaju, Cristina Nita-Rotaru, and Ian Molloy. Using Probabilistic Generative Models for Ranking Risks of Android Apps. In Proceedings of ACM CCS, 2012.
  18. S.Y. Yerima, S. Sezer, and I. Muttik. Android Malware Detection Using Parallel Machine Learning Classifiers. In Proceedings of NGMAST, Sept 2014.
  19. Yajin Zhou and Xuxian Jiang. Dissecting Android Malware: Characterization and Evolution. In Proceedings of the IEEE S&P, pages 95-109. IEEE, 2012.
  20. Fraud Detection in Social Networks. https://users.cs.fiu.edu/ _carbunar/caspr.lab/socialfraud.html.
  21. Google I/O 2013 - Getting Discovered on Google Play. www. youtube.com/watch?v=5Od2SuL2igA, 2013.
  22. Justin Sahs and Latifur Khan. A Machine Learning Approach to Android Malware Detection. In Proceedings of EISIC, 2012.
  23. Borja Sanz, Igor Santos, Carlos Laorden, Xabier Ugarte-Pedrero, Pablo Garcia Bringas, and Gonzalo ´Alvarez. Puma: Permission usage to detectmalware in android. In International Joint Conference CISIS12-ICEUTE´ 12-SOCO´ 12 Special Sessions, pages 289-298. Springer, 2013.
  24. Junting Ye and Leman Akoglu. Discovering opinion spammer groups by network footprints. In Machine Learning and Knowledge Discovery in Databases, pages 267-282. Springer, 2015.
  25. Leman Akoglu, Rishi Chandy, and Christos Faloutsos. Opinion Fraud Detection in Online Reviews by Network Effects. In Proceedings of ICWSM, 2013.
  26. Android Market API. https://code.google.com/p/ android-market-api/, 2011.
  27. Etsuji Tomita, Akira Tanaka, and Haruhisa Takahashi. The worstcase time complexity for generating all maximal cliques and computational experiments. Theor. Comput. Sci., 363(1):28-42, October 2006.
  28. Kazuhisa Makino and Takeaki Uno. New algorithms for enumerating all maximal cliques. 3111:260-272, 2004.
  29. Takeaki Uno. An efficient algorithm for enumerating pseudo cliques. In Proceedings of ISAAC, 2007.
  30. Steven Bird, Ewan Klein, and Edward Loper. Natural Language Processing with Python. O’Reilly, 2009.
  31. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. Thumbs Up? Sentiment Classification UsingMachine Learning Techniques. In Proceedings of EMNLP, 2002.
  32. John H. McDonald. Handbook of Biological Statistics. Sparky House Publishing, second edition, 2009.
  33. New Google Play Store greatly simplifies permissions. http://www.androidcentral.com/ new-google-play-store-4820-greatly-simplifies-permissions, 2014.
  34. Weka. http://www.cs.waikato.ac.nz/ml/weka/.
  35. S. I. Gallant. Perceptron-based learning algorithms. Trans. Neur. Netw., 1(2):179-191, June 1990.
  36. Leo Breiman. Random Forests. Machine Learning, 45:5-32, 2001.
  37. Ron Kohavi. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In Proceedings of IJCAI, 1995.
  38. D. H. Chau, C. Nachenberg, J. Wilhelm, A. Wright, and C. Faloutsos. Polonium: Tera-scale graph mining and inference for malware detection. In Proceedings of the SIAM SDM, 2011.
  39. Acar Tamersoy, Kevin Roundy, and Duen Horng Chau. Guilt by association: Large scale malware detection by mining file-relation graphs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and DataMining, KDD ’14, pages 1524-1533, New York, NY, USA, 2014. ACM

Publication Details

Published in : Volume 4 | Issue 2 | March-April 2018
Date of Publication : 2018-03-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 388-391
Manuscript Number : CSEIT184104
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

M. Kishore, P. Shirisha, "Analyzing Fake Ranks In Google Play", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 2, pp.388-391, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT184104

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