Search Rank Fraud and Malware Detection in Google Play

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Search Rank Fraud and Malware Detection in Google Play

Search Rank Fraud and Malware Detection in Google Play

Abstract

Projects on Search Rank Fraud Fraudulent behavior in Google Play, Android’s most popular app market, abuse of fuel search ranks,and proliferation of malware.projects reports on Search RankIn order to identify malware, previous work focused on executable apps and analysis of permissions.In classifying gold standard malware datasets, fraudulent and legitimate applications, FairPlay achieves more than 95 percent accuracy. We show that 75 percent of identified malware applications commit fraud in the search rank. FairPlay finds hundreds of fraudulent apps that are currently evading Google Bouncer’s detection technology.

Introduction

Google Play first releases its 2008 app because it distributes apps to all Android users.In the Google Play Store, it provides services that allow users to discover the particular application, purchase those applications and install it on their mobile devices.Since Android is an open source environment, the application developers can easily access all the details about the users of the application via Google Play.Google Play has 1.8 million mobile applications available that are downloaded by more than 25 billion users worldwide.

Fraudulent developers use search ranking algorithms to promote their apps to the top while searching.However, fraudulent developers give fake ratings and reviews about their application to promote their application to the top.Google Play uses two typical approaches to detect malware.

Conclusion

We developed a fraud detection system for mobile apps in this search rank fraud and malware detection project in Google Play. Specifically, we first showed that fraud occurred in leading sessions and provided a method for leading mining sessions for each app from its historical ranking records.projects reports on Search Rank We have identified that rating, review-based evidence is considered for the detection of the ranking.

Projects on Search Rank Fraud In addition, we proposed an optimization-based aggregation method for integrating all the evidence to assess the credibility of leading mobile apps sessions.Finally, we validate the proposed system with extensive experiments on real-world App data collected from the Apple App Store.projects reports on Search Rank Experimental results showed the proposed approach’s effectiveness.We plan to study more effective evidence of fraud in the future and analyze the latent relationship between rating, review, and rankings.