Ranking fraud in the mobile Apps is nothing but the false or fake activities which are going to be done in Apps
popularity list for bumping up the fraud App . It is very easy for the App developer to use the fake App and fake rating
for commiting the ranking fraud . The research in ranking fraud of the mobile App is limited . For that purpose a
ranking fraud detection system and the App recommendation system is proposed . There are mainly three types of
evidences , Ranking based evidences , Rating based evidences , and Review based evidences . These evidences can be
obtained from Apps ranking , rating and review history .Then proposed an optimization based aggregation method for
integrating all these evidences for the fraud detection .In this way more effectiveness and regularity of the ranking
fraud detection system is obtained and the original App is recommended to the user.
Ranking fraud in the mobile Apps is nothing but the false or fake activities which are going to be done in Apps
popularity list for bumping up the fraud App . It is very easy for the App developer to use the fake App and fake rating
for commiting the ranking fraud . The research in ranking fraud of the mobile App is limited . For that purpose a
ranking fraud detection system and the App recommendation system is proposed . There are mainly three types of
evidences , Ranking based evidences , Rating based evidences , and Review based evidences . These evidences can be
obtained from Apps ranking , rating and review history .Then proposed an optimization based aggregation method for
integrating all these evidences for the fraud detection .In this way more effectiveness and regularity of the ranking
fraud detection system is obtained and the original App is recommended to the user.
DETECTING MALICIOUS FACEBOOK APPLICATIONS - IEEE PROJECTS IN PONDICHERRY,BUL...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Third-party apps are a major reason for the popularity and addictiveness of Facebook. Unfortunately, hackers have realized the potential of using apps for spreading malware and spam. The problem is already significant, as we find that at least 13% of apps in our dataset are malicious. So far, the research community has focused on detecting malicious posts and campaigns. First, we identify a set of features that help us distinguish malicious apps from benign ones. For example, we find that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps.
>Contact me on mangenashiva@gmail.com for full documentaion and code
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
Mobile apps-user interaction measurement & Apps ecosystemSalah Amean
This presentation talks about the user behaviours and the trend of mobile applications. It also talks about the behaviour of users downloading most common application on the market.
There are many malicious programs disbursing on Face book every single day. Within the recent occasions, online hackers have thought about recognition within the third-party application platform additionally to deployment of malicious programs. Programs that present appropriate method of online hackers to spread malicious content on Face book however, little is known concerning highlights of malicious programs and just how they function. Our goal ought to be to create a comprehensive application evaluator of face book the very first tool that will depend on recognition of malicious programs on Face book. To develop rigorous application evaluator of face book we utilize information that's collected by way of observation of posting conduct of Face book apps that are seen across numerous face book clients. This can be frequently possibly initial comprehensive study which has dedicated to malicious Face book programs that concentrate on quantifying additionally to knowledge of malicious programs making these particulars in to a effective recognition method. For structuring of rigorous application evaluator of face book, we utilize data within the security application within Facebook that examines profiles of Facebook clients.
Fraud and Malware Detection in Google Play by using Search Rankijtsrd
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. . Fair Play discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. A. Brahma Reddy | K. V. Ranga Rao | V. Vinay Kumar "Fraud and Malware Detection in Google Play by using Search Rank" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35728.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35728/fraud-and-malware-detection-in-google-play-by-using-search-rank/a-brahma-reddy
Quality Over Quantity - Mobile Users Mater | Jarah Euston, FlurryDealmaker Media
Jarah Euston, Director, Analytics, Flurry
How to identify, acquire and measure quality users for your mobile app.
The cost of building a quality app audience is dramatically increasing. Cost and quality of acquisition varies widely by channel, changes over time and different purchase volumes. To optimize your ROI of marketing spend, you can identify which channels deliver the best users based on engagement, monetization and more. Not every ad unit is created equal.
Research of usability of Mashup Tools done for Kent County Council as part of the Pic and Mix Pilot (2009), opening up Kent related datasets for all to use and exploit.
Introducing Crystal Semantics, the worlds first Sense Engine. Designed to enable ads to be placed in true context, this technology is driving a substantial number of ad campaigns
DISCOVERY OF RANKING FRAUD FOR MOBILE APPS - IEEE PROJECTS IN PONDICHERRY,BUL...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
A survey on identification of ranking fraud for mobile applicationseSAT Journals
Abstract Now a day, mobile App is an exceptionally prevalent and surely understood idea because of the quick progression in the portable innovation and cell phones. Because of the extensive number of versatile Apps, ranking fraud is the key test before the versatile App market. In this paper we are proposing a ranking fraud discovery framework for portable Apps. The proposed framework mines the leading sessions, for example, leading sessions of portable applications to precisely find the ranking fraud. Other than this, by displaying Apps ranking, rating and review practices utilizing measurable theories tests, we examine three sorts of confirmations, they are ranking based proofs, rating based proofs and review based confirmations. Proposed an aggregation method to combine all the proof for fraud detection. Keywords: Ranking Fraud, Fraud For Mobile, Identification Mobile Fraud
How to make sure that any mobile app that you are marketing ranks well in the AppStore. Strong SEO strategy in the submission of your application will help to drive long term downloads and traffic to your app.
DETECTING MALICIOUS FACEBOOK APPLICATIONS - IEEE PROJECTS IN PONDICHERRY,BUL...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Third-party apps are a major reason for the popularity and addictiveness of Facebook. Unfortunately, hackers have realized the potential of using apps for spreading malware and spam. The problem is already significant, as we find that at least 13% of apps in our dataset are malicious. So far, the research community has focused on detecting malicious posts and campaigns. First, we identify a set of features that help us distinguish malicious apps from benign ones. For example, we find that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps.
>Contact me on mangenashiva@gmail.com for full documentaion and code
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
Mobile apps-user interaction measurement & Apps ecosystemSalah Amean
This presentation talks about the user behaviours and the trend of mobile applications. It also talks about the behaviour of users downloading most common application on the market.
There are many malicious programs disbursing on Face book every single day. Within the recent occasions, online hackers have thought about recognition within the third-party application platform additionally to deployment of malicious programs. Programs that present appropriate method of online hackers to spread malicious content on Face book however, little is known concerning highlights of malicious programs and just how they function. Our goal ought to be to create a comprehensive application evaluator of face book the very first tool that will depend on recognition of malicious programs on Face book. To develop rigorous application evaluator of face book we utilize information that's collected by way of observation of posting conduct of Face book apps that are seen across numerous face book clients. This can be frequently possibly initial comprehensive study which has dedicated to malicious Face book programs that concentrate on quantifying additionally to knowledge of malicious programs making these particulars in to a effective recognition method. For structuring of rigorous application evaluator of face book, we utilize data within the security application within Facebook that examines profiles of Facebook clients.
Fraud and Malware Detection in Google Play by using Search Rankijtsrd
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. . Fair Play discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. A. Brahma Reddy | K. V. Ranga Rao | V. Vinay Kumar "Fraud and Malware Detection in Google Play by using Search Rank" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35728.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35728/fraud-and-malware-detection-in-google-play-by-using-search-rank/a-brahma-reddy
Quality Over Quantity - Mobile Users Mater | Jarah Euston, FlurryDealmaker Media
Jarah Euston, Director, Analytics, Flurry
How to identify, acquire and measure quality users for your mobile app.
The cost of building a quality app audience is dramatically increasing. Cost and quality of acquisition varies widely by channel, changes over time and different purchase volumes. To optimize your ROI of marketing spend, you can identify which channels deliver the best users based on engagement, monetization and more. Not every ad unit is created equal.
Research of usability of Mashup Tools done for Kent County Council as part of the Pic and Mix Pilot (2009), opening up Kent related datasets for all to use and exploit.
Introducing Crystal Semantics, the worlds first Sense Engine. Designed to enable ads to be placed in true context, this technology is driving a substantial number of ad campaigns
DISCOVERY OF RANKING FRAUD FOR MOBILE APPS - IEEE PROJECTS IN PONDICHERRY,BUL...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
A survey on identification of ranking fraud for mobile applicationseSAT Journals
Abstract Now a day, mobile App is an exceptionally prevalent and surely understood idea because of the quick progression in the portable innovation and cell phones. Because of the extensive number of versatile Apps, ranking fraud is the key test before the versatile App market. In this paper we are proposing a ranking fraud discovery framework for portable Apps. The proposed framework mines the leading sessions, for example, leading sessions of portable applications to precisely find the ranking fraud. Other than this, by displaying Apps ranking, rating and review practices utilizing measurable theories tests, we examine three sorts of confirmations, they are ranking based proofs, rating based proofs and review based confirmations. Proposed an aggregation method to combine all the proof for fraud detection. Keywords: Ranking Fraud, Fraud For Mobile, Identification Mobile Fraud
How to make sure that any mobile app that you are marketing ranks well in the AppStore. Strong SEO strategy in the submission of your application will help to drive long term downloads and traffic to your app.
Mobile 1UP was invited to speak at a workshop focused around development and go-to-market and strategies around the iphone platform. It is an event specifically hosted by the Stockholm School of Economics as part of the "Entrepreneurship & Coffee" after-work workshops for emerging entreprenuers in the nordic region.
Project center in trichy @ieee 2016 17 titles for java and dotnetElakkiya Triplen
IEEE Final Year Projects for M.E/M.TECH,B.E-CSE/IT from any domain & Technologies.For more detail contact:-DreamWeb TechnoSolutions@7200021403/04, 73/5 3rd floor,Kamatchi cmplx,Thillai nagar 1st cross,Trichy.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Knowledge and Data Engineering IEEE 2015 ProjectsVijay Karan
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
Knowledge and Data Engineering IEEE 2015 ProjectsVijay Karan
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
this is VTU FINAL YEAR PROJECT REPORT full report is attached below.this alone with front pages attached Front pages report follows all the guidelines specified by vtu according to our college.
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
Netspam: An Efficient Approach to Prevent Spam Messages using Support Vector ...ijtsrd
The most common mode for consumers to express their level of satisfaction with their purchases is through online ratings, which we can refer as Online Review System. Network analysis has recently gained a lot of attention because of the arrival and increasing attractiveness of social sites, such as blogs, social networks, micro blogging, or customer review sites. The reviews are used by potential customers to find opinions of existing users before purchasing the products. Online review systems play an important part in affecting consumers actions and decision making, and therefore attracting many spammers to insert fake feedback or reviews to manipulate review content and ratings. Malicious users misuse the review website and post untrustworthy, low quality, or sometimes fake opinions, which are referred as Spam Reviews. In this study, we aim at classifying reviews as positive, negative and spam reviews by creating a social network similar platform and providing communication between users in it. P. Sai Kiran | K. Prudhvi Chowdary | T. T. Venkata Rayudu | K. Vinay Kumar"Netspam: An Efficient Approach to Prevent Spam Messages using Support Vector Machine" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11419.pdf http://www.ijtsrd.com/computer-science/computer-network/11419/netspam-an-efficient-approach-to-prevent-spam-messages-using-support-vector-machine/p-sai-kiran
Tug of Perspectives: Mobile App Users vs Developers (pp. 83-94)
Sandeep Kaur Kuttal (#1), Yiting Bai (#2), Ezequiel Scott (∗3), Rajesh Sharma (∗4),
(#) Tandy School of Computer Science, University of Tulsa, USA.
(∗) University of Tartu, Estonia.
Vol. 18 No. 6 JUNE 2020 International Journal of Computer Science and Information Security
https://sites.google.com/site/ijcsis/vol-18-no-6-jun-2020
Similar to Discovery of ranking fraud for mobile apps (20)
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
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Discovery of ranking fraud for mobile apps
1. DISCOVERY OF RANKING FRAUD FOR MOBILE APPS
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 global anomaly 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
2. validate the effectiveness of the proposed system, and show the scalability of the
detection algorithm as well as some regularity of ranking fraud activities.
EXISTING SYSTEM
Generally speaking, the related works of this study can be grouped into three
categories. The first category is about web ranking spam detection. Specifically,
the web ranking spam refers to any deliberate actions which bring to selected
webpages an unjustifiable favorable relevance or importance. For example,
Ntoulas et al. have studied various aspects of content-based spam on the web and
presented a number of heuristic methods for detecting content based spam. Zhou et
al. have studied the problem of unsupervised web ranking spam detection.
Specifically, they proposed an efficient online link spam and term spam detection
methods using spamicity. Recently, Spirin and Han have reported a survey on web
spam detection, which comprehensively introduces the principles and algorithms in
3. the literature. Indeed, the work of web ranking spam detection is mainly based on
the analysis of ranking principles of search engines, such as PageRank and query
term frequency. This is different from ranking fraud detection for mobile Apps.
The second category is focused on detecting online review spam. For example,
Lim et al] have identified several representative behaviors of review spammers and
model these behaviors to detect the spammers. Wu et al. have studied the problem
of detecting hybrid shilling attacks on rating data. The proposed approach is based
on the semisupervised learning and can be used for trustworthy product
recommendation. Xie et al. have studied the problem of singleton review spam
detection. Specifically, they solved this problem by detecting the co-anomaly
patterns in multiple review based time series. Although some of above approaches
can be used for anomaly detection from historical rating and review records, they
are not able to extract fraud evidences for a given time period (i.e., leading
session). Finally, the third category includes the studies on mobile App
recommendation. For example, Yan and Chen developed a mobile App
recommender system, named Appjoy, which is based on user’s App usage records
to build a preference matrix instead of using explicit user ratings. Also, to solve the
sparsity problem of App usage records, Shi and Ali studied several
recommendation models and proposed a content based collaborative filtering
model, named Eigenapp, for recommending Apps in their website Getjar. In
4. addition, some researchers studied the problem of exploiting enriched contextual
information for mobile App recommendation. For example, Zhu et al. proposed a
uniform framework for personalized context-aware recommendation, which can
integrate both context independency and dependency assumptions. However, to the
best of our knowledge, none of previous works has studied the problem of ranking
fraud detection for mobile Apps.
PROPOSED SYSTEM:
First, the download information is an important signature for detecting ranking
fraud, since ranking manipulation is to use so-called “bot farms” or “human water
armies” to inflate the App downloads and ratings in a very short time. However,
the instant download information of each mobile App is often not available for
analysis. In fact, Apple and Google do not provide accurate download information
on any App. Furthermore, the App developers themselves are also reluctant to
release their download information for various reasons. Therefore, in this paper,
we mainly focus on extracting evidences from Apps’ historical ranking, rating and
review records for ranking fraud detection. However, our approach is scalable for
integrating other evidences if available, such as the evidences based on the
download information and App developers’ reputation. Second, the proposed
5. approach can detect ranking fraud happened in Apps’ historical leading sessions.
However, sometime, we need to detect such ranking fraud from Apps’ current
ranking observations. Actually, given the current ranking ra now of an App a, we
can detect ranking fraud for it in two different cases. First, if ra now > K_, where
K_ is the ranking threshold introduced in Definition 1, we believe a does not
involve in ranking fraud, since it is not in a leading event. Second, if ra now < K_,
which means a is in a new leading event e, we treat this case as a special case that
Te end ¼ te now and u2 ¼ 0. Therefore, such real-time ranking frauds also can be
detected by the proposedapproach.
Module 1
Leading events
Definition 1 (Leading Event). Given a ranking threshold K e¼½testart;teend_ and
corresponding rankings of a, which satisfies Ra start _ K _ a start_1 <r , and r a end
_ K _ <r a endþ1 . Moreover, 8t k 2ðt e start ;t e end Þ, we have r a k . Note that
we apply a ranking threshold K _ K _ _ which is usually smaller than K here
because K may be very big (e.g., more than 1,000), and the ranking records beyond
K _ (e.g., 300) are not very useful for detecting the ranking manipulations.
Furthermore, we also find that some Apps have several adjacent leading events
which are close to each other and form a leading session. For example, Fig. 2b
6. shows an example of adjacent leading events of a given mobile App, which form
two leading sessions. Particularly, a leading event which does not have other
nearby neighbors can also be treated as a special leading session.
Module 2
Leading Sessions
A leading session s of App a contains a time range T s ¼½t s start ;t s end _ and n
adjacent leading events fe 1 ; ...;e n g, which satisfies t and there is no other leading
session s s start _ ¼ t e 1 start , t that makes T _ . Meanwhile, 8i ½1;nÞ, we have
ðt e iþ1 start _ t e I end s end s I ¼ t _ _ T Þ < f, where f is a predefined time
threshold for merging leading events. Intuitively, the leading sessions of a mobile
App represent its periods of popularity, so the ranking manipulation will only take
place in these leading sessions. Therefore, the problem of detecting ranking fraud
is to detect fraudulent leading sessions. Along this line, the first task is how to mine
the leading sessions of a mobile App from its historical ranking records.
Module 3
Identifying the Leading Sessions forMobile APPs
There are two main steps for mining leading sessions. First, we need to discover
leading events from the App’s historical ranking records. Second, we need to
7. merge adjacent leading events for constructing leading sessions. Specifically,
Algorithm demonstrates the pseudo code of mining leading sessions for a given
App In Algorithm , we denote each leading event e and session s as tuples <t e
start ;t e end > and <t s start > respectively, where E is the set of leading events in
session s. Specifically, we first extract individual leading event e for the given App
a (i.e., Step 2 to 7) from the beginning time. For each extracted individual leading
event e,we check the time span between e and the current leading session s to
decide whether they belong to the same leading session based on Definition 2.
Particularly, if ðt s e start ;t _ t Þ < f, will be considered as a new leading session
(i.e., Step 8 to 16). Thus, this algorithm can identify leading events and ses- sions
by scanning a’s historical ranking records only once. S end s end ;E s
CONCLUDING REMARKS
In this paper, we developed a ranking fraud detection system for mobile Apps.
Specifically, we 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, we identified ranking based evidences, rating
based evidences and review based evidences for detecting ranking fraud.
Moreover, we proposed an optimization based aggregation method to integrate all
the evidences for evaluating the credibility of leading sessions from mobile Apps.
8. An unique perspective of this approach is that all the evidences can be modeled by
statistical hypothesis tests, thus it is easy to be extended with other evidences from
domain knowledge to detect ranking fraud. Finally, we validate the proposed
system with extensive experiments on real-world App data collected from the
Apple’s App store. Experimental results showed the effectiveness of the proposed
approach. In the future, we plan to study more effective fraud evidences and
analyze the latent relationship among rating, review and rankings. Moreover, we
will extend our ranking fraud detection approach with other mobile App related
services, such as mobile Apps recommendation, for enhancing user experience.
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