SEARCH RANK FRAUD AND MALWARE DETECTION IN GOOGLE PLAY

1croreprojects@gmail.com

ABSTRACT

Fraudulent behaviors in Google Play, the most popular Android app market, fuel search rank abuse and 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 (87 K apps, 2.9 M reviews, and 2.4M reviewers, collected over half a year), in order to identify suspicious apps. FairPlay achieves over 95 percent accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75 percent 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.

 

EXISTING SYSTEM:

The mobile industry is developing rapidly; therefore the numbers of mobile applications are increasing day by day in the market. As there are many apps available in market users are in fuzzy state while downloading the apps for their use. Different App stores like Google play store and Apple store launched their leader board on daily basis to inspire the users to download most popular applications by observing the ranking of applications. In fact to advertise a particular mobile Apps, leader board of apps is the most important way in the market. An app which is at the top on the leader board leads to large number of downloads and it will gain maximum profit. In order to have their Apps ranked as high as possible, app developers promote their apps using various ways such as advertising, offers etc. Such applications damage to phone and also may cause data thefts. Hence such applications must be identified, so that they will be identifiable for play store users. 

 

DISADVANTAGES:

Due to the huge number of mobile Apps, it is difficult to manually label ranking fraud for each app, so it is important to automatically detect fraud without using any basic information. 

Mobile Apps are not always ranked high in the leader board, but only in some leading events ranking that is fraud usually happens in leading sessions.

 

PROPOSED SYSTEM:

We are proposing an android application which will process the information, comments and three reviews of the application with natural language processing to give results. So it will be easier. Specifically, they first showed that ranking fraud happened in leading sessions and provided a method for mining leading sessions for each App from its historical ranking records. Then, they identified ranking based evidences, rating based evidences and review based evidences for detecting ranking fraud to decide fraud application. FairPlay, a system to detect both fraudulent and malware Google Play apps. They studied on a newly contributed longitudinal app dataset, in which they had shown a high percentage of malware is involved in search rank fraud; both are accurately identified by FairPlay. In addition, they showed FairPlay’s ability to discover hundreds of apps that evade Google Play’s detection technology, including a new type of coercive fraud attack.

 

 

 

ADVANTAGES:

We are proposing a web application which will process the information, comments and thee reviews of the application with natural language processing to give results in the form of graph.

So it will be easier to decide which application is fraud or not. Multiple applications can be processed at a time with the web application.

In this way it will find out score of each of the reviews and determine whether app is fraud or not on the basis of review based evidences

 

LITERATURE SURVEY

 

1.TITLE:  “Opinion Fraud Detection in Online Reviews by Network Effects,” in Proc. 7th Int. AAAI Conf. Weblogs Soc. Media, 2013, pp. 2–11.

 

AUTHORS: L. Akoglu, R. Chandy, and C. Faloutsos

 

ABSTRACT:

User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However, review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size. We demonstrate the effectiveness of our framework on synthetic and real datasets; where FRAUDEAGLE successfully reveals fraud-bots in a large online app review database. 

2. TITLE: Discovery of Ranking Fraud for Mobile Apps

 

AUTHORS: Hengshu Zhu, Hui Xiong

 

ABSTRACT:

Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of globalanomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps ranking, rating and review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

3. TITLE: Survey on Fraud Ranking in Mobile Apps

 

AUTHORS: Monali Zende, Aruna Gupta

 

ABSTRACT:

Ranking fraud  in the mobile App business suggest  to false  or tricky  exercises which have a motivation behind, knocking  up the Apps  in the fame list. Now a days, many shady means are used more frequently  by app developers, such  expanding their Apps   business  or posting imposter App evaluations,  to confer positioning misrepresentation. There is a limited understanding and research  area for preventing ranking fraud.  This paper gives a whole perspective  of positioning misrepresentation and describes a Ranking fraud  identification framework for mobile Apps.  This work is grouped into three category. First  is web ranking spam detection, second  Is online review spam detection and last one  is mobile  app recommendation. The Web ranking spam refers  to any deliberate actions which  bring  to selected Web pages  an unjustifiable favorable relevance  or importance. Review spam  is designed to give unfair view  of some  products  so as to influence the consumers perception  of the products by directly or indirectly influating or damaging the product s  reputation.

4. TITLE: MobSafe: Forensic Analysis for Android Applications and detection Of Fraud Apps Using CloudStack and Data Mining

AUTHORS: Patil Rohini, Kale Pallavi

ABSTRACT:

Nowadays there are so many applications available  on internet because of that user can not always get correct or  true reviews about the product on internet. So we can check for  more than 2 sites, for reviews of same product. The reviews may be fake on individual sites. But after comparing reviews from 2 sites we can get more clear idea. Hence we can get  higher probability of getting real reviews. So we are proposing  a system to develop a android application that will take reviews  from two different websites for single product, and analyze  them with NLP for positive negative rating. In this , User will give 2 different URLs from 2 different sites for same product to  system as input.For every URL Reviews and comments will  be fetched separately and analyzed with NLP for positive negative  rating .Then their rating will be combined together with  average to give final rating for the product. In this paper we  propose the system to develop a android app which help to  detect fraud apps using cloudstack and data mining. To  develop propose system we use two methods natural language  processing and Kmeans algorithm.

 

5. TITLE: FairPlay: Fraud and Malware Detection in Google Play   

 

AUTHORS: Mahmudur Rahman, Mizanur Rahman

 

ABSTRACT:

Fraudulent behaviors in Google’s Android app market fuel search rank abuse and malware proliferation. We present FairPlay, a novel system that uncovers both malware and search rank fraud apps, by picking out trails that fraudsters leave behind. To identify suspicious apps, FairPlay’s PCF algorithm correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from longitudinal Google Play app data. We contribute a new longitudinal app dataset to the community, which consists of over 87K apps, 2.9M reviews, and 2.4M reviewers, collected over half a year. 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, and reveals a new type of attack campaign, where users are harassed into writing positive reviews, and install and review other apps.

 

MODULES

This system consists of three modules described as follows:

• Rating Based Evidences 

• Review Based Evidences 

• Ranking Based Evidence

 

LOGIN:

• USER LOGIN

• ADMIN LOGIN

 

USER LOGIN:

 

Register:

  User registers their details. That registration contains username, password, DOB, mailed, mobile number.

 

  Login:

After registered user details they can able to login. The user name and password must be registered into the database then only user able to login otherwise popup box will show along with invalid details.

Rating Based Evidences:

After downloading an app users generally rate the app. The rating given by the user is one of the most important factors for the popularity of the app. An app having higher rating always attracts more number of users to download it and naturally it can also be ranked higher in the chart rankings. 

Review Based Evidences

Along with rating users are allowed to write their reviews about the app. Such reviews are showing the personalized experiences of usage for particular mobile Apps. The review given by the user is one of the most important factors for the popularity of the app. As the reviews are given in natural language so preprocessing of reviews and then sentiment analysis on preprocessed reviews is performed. The system will find sentiment of the review which can be

Positive or negative.

Ranking Based Evidences

It will classify the review as positive or negative. The system will find sentiment of the review which can be positive or negative. Positive review adds plus one to positive score, if negative it will add one to negative score. In this phase, we detect Apps’ ranking behavior, by finding three phases of ranking, namely, rising phase, maintaining phase and recession phase. If the apps ranking reach to peak position in the leader board that phase is called as rising phase and maintaining same peak position for specific time period is called as maintaining phase. If the ranking of the app decreases rapidly in the leading event then it is called as recession phase.

 

ADMIN:

• After admin login add new applications and view all user ratings, reviews, recommendations and ranking process.

• Admin added all positive & negative words

• Admin can view who are all made malware in google play

 

ATTACKER:

• Attacker login with fake id and password

• After login they view all application what are all in google play

• They can choose application and enter the review

 

• If attacker enters positive review it can be added 3 into the app ranking. Similarly if enter negative review it can be added 3 into app negative ranking.

 

RESULT:

We showing this google play app ranking result in graph representation.

• The graph first it represent overall ranking about the googleplay apps both positive and negative based on from original users and malware users

• Second one represent registered user review based ranking

• Third one represent malware user review based ranking

 

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

 

System:Pentium IV 2.4 GHz.

Hard Disk :40 GB.

Floppy Drive:1.44 Mb.

Monitor:15 VGA Colour.

Mouse:Logitech.

 

SOFTWARE REQUIREMENTS:

 

Operating system:Windows XP/7.

Coding Language:ASP.net, C#.net

Tool:Visual Studio 2010

Database:SQL SERVER 2008 R2

 

REFERENCES:

[1] Google Play. [Online]. Available: https://play.google.com/

[2] E. Siegel, “Fake reviews in Google Play and Apple App Store,” Appentive, Seattle, WA, USA, 2014.

[3] Z. Miners. (2014, Feb. 19). “Report: Malware-infected Android apps spike in the Google Play store,” PC World. Available: http:// www.pcworld.com/article/2099421/report-malwareinfected androidapps-spike-in-the-google-play-store.html

[4] 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

[5] 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/

 



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