Discovery of ranking fraud for mobile apps
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This document proposes a system to detect ranking fraud for mobile apps. It investigates three types of evidence: ranking, rating, and review behaviors. The existing systems for related problems like web spam and review spam detection are insufficient for detecting ranking fraud in apps. The proposed system accurately locates fraudulent ranking periods in apps, and uses an optimization method to aggregate all evidence types to identify ranking fraud. It was evaluated on real app data and performed effectively at scale.
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The document discusses a proposed system for detecting ranking fraud for mobile apps. It begins by describing existing ranking fraud and some current detection systems. It then outlines the proposed system which first identifies "leading sessions" in an app's historical ranking data that indicate periods of popularity. It then detects fraud by analyzing ranking, rating, and review behaviors during these sessions using statistical tests. Finally, the proposed system aggregates all the evidence to evaluate sessions for fraud and was tested on real app store data.
Discovery of ranking fraud for mobile apps
1. The document proposes a ranking fraud detection system for mobile apps. It aims to detect fraudulent activities aimed at artificially boosting an app's popularity ranking.
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3. An optimization-based method is used to aggregate all evidence to evaluate the credibility of leading sessions and detect ranking fraud. The system is evaluated on real app data and shown to effectively detect fraud while scaling to the large number of apps.
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1. The document proposes a ranking fraud detection system for mobile apps. It aims to detect fraudulent activities aimed at artificially boosting an app's popularity ranking.
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3. An optimization-based method is used to aggregate all evidence to evaluate the credibility of leading sessions and detect ranking fraud. The system is evaluated on real app data and shown to effectively detect fraud while scaling to the large number of apps.
Discovery of ranking fraud for mobile apps
This document proposes a system to detect ranking fraud for mobile apps. It aims to address the challenges of detecting local anomalies rather than global anomalies, doing so at scale without manual labeling, and identifying implicit fraud patterns. The system first identifies active periods called "leading sessions" in apps' historical ranking data. It then analyzes three types of evidence - ranking, ratings, and reviews - using statistical tests to model apps' behaviors. An optimization method integrates the evidence for fraud detection evaluation. The approach was validated on real app data, showing effectiveness and scalability. The key advantages are addressing when fraud occurs and enabling automatic detection at scale.
Discovery of ranking fraud for mobile apps
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The document describes the design of two software engineering case studies using Rational Rose:
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A survey on identification of ranking fraud for mobile applications
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Similar to Discovery of ranking fraud for mobile apps
IJET-V3I1P2
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.
IRJET- Discovery of Fraud Apps Utilizing Sentiment Analysis
This document discusses a proposed system for detecting fraudulent mobile apps through sentiment analysis. It begins with an introduction describing the problem of ranking fraud in app markets and an overview of the proposed solution. It then reviews related work on ranking spam detection, online review spam detection, and mobile app recommendation. The proposed system is described as using natural language processing on app reviews, comments and data to identify ranking, rating and behavior patterns that could indicate fraud. The goals are outlined as detecting ranking fraud in mobile apps and improving fraud detection efficiency. Key components of the system include mining app historical ranking data to identify periods of abnormal high ranking that could be fraudulent. Multiple sources of evidence like ranking, rating and review-based patterns would be analyzed and aggregated to
A DATA MINING FRAMEWORK FOR PREVENTION OF FAKE APPLICATIONS USING OPINION MINING
This document summarizes a proposed data mining framework to prevent fake mobile applications using opinion mining and sentiment analysis. The framework would analyze user reviews and sentiments expressed about various apps to determine whether they are genuine or fake. It would collect app data and user reviews over long periods from app stores. By using data mining techniques like sentiment analysis and considering factors like review patterns and app usage trends, the framework aims to detect fake apps that try to manipulate reviews and catch those with anomalous review or positioning behaviors compared to authentic apps. The proposed system is intended to provide users with genuine reviews and insights to avoid downloading fraudulent apps.
IRJET- Detection of Ranking Fraud in Mobile Applications
The document proposes a system to detect ranking fraud in mobile applications. It identifies three types of evidence - ranking based, rating based, and review based - that can be used to detect fraudulent behavior. The proposed system first identifies leading sessions of apps based on historical ranking records. It then analyzes apps' ranking, rating, and review patterns within leading sessions to find anomalies compared to normal apps. This evidence is aggregated to reliably identify fraudulent apps in the mobile market. The system aims to enhance user experience by potentially incorporating the fraud detection into a recommender system.
Discovery of ranking fraud for mobile apps
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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
DISCOVERY OF RANKING FRAUD FOR MOBILE APPS
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
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VTU final year project report Main
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.
IRJET - Discovery of Ranking Fraud for Mobile Apps
1) The document proposes a system for detecting ranking fraud in mobile apps. It collects reviews data and performs preprocessing including stop word filtering and stemming.
2) It then extracts features from the reviews through part-of-speech tagging and sentiment analysis. Three types of evidence are identified: ranking, rating, and review evidence.
3) An evidence aggregation method is proposed to integrate the different evidence types and classify apps as fraudulent or genuine based on the sentiment polarity of reviews. The system aims to help users identify legitimate apps and enhance their experience.
Discovery of Ranking Fraud for Mobile Apps
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)
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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
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Tamin Nadu, INDIA - 600 026
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website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Discovery of Ranking Fraud for Mobile Apps
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
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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
Survey on Fraud Malware Detection in Google Play Store
This document discusses methods for detecting fraud and malware in mobile applications on the Google Play Store. It proposes an incremental learning framework that aggregates evidence from an app's ratings, reviews, and rankings over time to detect suspicious changes that may indicate fraud. The framework characterizes large datasets of apps and can be extended with additional evidence sources. An experiment on real app data validated that the proposed approach more effectively detects fraud compared to existing methods. The framework provides accurate fraud assessments while being scalable to the large number of apps on Google Play.
Fraud App Detection using Machine Learning
This document proposes a system to detect fraudulent mobile apps using machine learning. It analyzes apps based on four parameters: in-app purchases, ads, user ratings, and reviews. Three machine learning models are compared: Decision Tree, Logistic Regression, and Naive Bayes. The model with the highest accuracy for predicting fraudulent apps based on these parameters will be selected. Prior research on detecting app ranking fraud and fraudulent apps using sentiment analysis is reviewed. The proposed system architecture involves collecting data on apps from the Google Play Store and using machine learning to classify apps as fraudulent or not fraudulent.
Knowledge and Data Engineering IEEE 2015 Projects
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 Projects
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
Fraud and Malware Detection in Google Play by using Search Rank
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2. FairPlay generates relational, behavioral and linguistic features from apps to train machine learning models to classify apps as fraudulent, benign, or malware.
3. Experimental results show that FairPlay achieves over 97% accuracy in detecting fraudulent apps and over 95% accuracy in detecting malware apps, outperforming existing approaches. FairPlay also discovered hundreds of fraudulent apps not detected by Google's systems.
Detection of Fake reviews
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Choosing the Right Testing Strategy to Scale up Mobile App Testing.pdf
The document discusses the importance of developing a robust mobile app testing strategy to handle the challenges of mobile app testing at scale. It outlines 14 key elements that should be considered when creating a testing strategy, including device selection, deciding between automated and manual testing, network connectivity testing, performance testing, and security testing. The document stresses the need for a balanced approach that blends automated and manual testing techniques to effectively test mobile apps.
Detecting malicious facebook applications
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
Zero day malware detection
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Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Discovery of ranking fraud for mobile apps
- 1. Discovery of Ranking Fraud for Mobile Apps Presented By Naresh
- 2. TABLE OF CONTENTS Abstract Existing System Proposed System Hardware Requirements Software Requirements
- 3. 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. A holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. 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.
- 4. Existing System In Existing system, while there are some related work, such as web ranking spam detection, online review spam detection and mobile App recommendation, the problem of detecting ranking fraud for mobile Apps is still under-explored. This study can be grouped into three categories. first category :- web ranking spam detection. second category :-detecting online review spam. third category:-mobile App recommendation
- 5. Disadvantages Although some of the existing 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). Cannot able to detect ranking fraud happened in Apps’ historical leading sessions There is no existing benchmark to decide which leading sessions or Apps really contain ranking fraud.
- 6. Proposed System we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. we propose an optimization based aggregation method to integrate all the evidences for fraud detection. we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period.
- 7. Advantages The proposed framework is scalable and can be extended with other domain generated evidences for ranking fraud detection. Experimental results show the effectiveness of the proposed system, the scalability of the detection algorithm as well as some regularity of ranking fraud activities. we develop four intuitive baselines and invite five human evaluators to validate the effectiveness of our approach Evidence Aggregation based Ranking Fraud Detection (EA-RFD).
- 8. Hardware Requirements System : Pentium IV 2.4 GHz. Hard Disk : 80 GB and above Ram : 512 MB and above.
- 9. Software Requirements Operating system : Windows XP/7. Coding Language : JAVA/J2EE IDE : Netbeans 7.4 Database : MYSQL
- 10. THANK YOU