CHAMP: Characterizing Undesired App Behaviors from User Comments based on Market Policies (original) (raw)

Fraud App Detection Using Sentiment Analysis

International Journal of Advance Research in Computer Science and Management Studies [IJARCSMS] ijarcsms.com

Developers have developed a ranking fraud detection system for mobile Apps. Specifically, we show that ranking fraud happened in the leading sessions and provided a method for mining leading sessions for each App from its historical ranking records. Then, we identify ranking based evidences and rating based evidences for detecting ranking fraud. Moreover, we

Identification of Fraud Apps Using Sentiment Analysis Techniques

2021

Considering that application's security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to spot insecure apps by analyzing user feedback on Google Play platform using sentiment analysis. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application. But unfortunately, not all of these reviews are real, fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews and deliver accurate and correct results. This tool is useful for both users wanting to install an android app and for developers interested in app's optimization.

IRJET- Discovery of Ranking Fraud for Mobile Apps

IRJET, 2020

Deceptive activities in the app are done in order to appear on the top ranking list of apps. Nowadays, many App developers use shady means such as increasing their app sales or posting phony app ratings to commit ranking fraud. Preventing the ranking fraud has nowadays become an important criteria but there is less research done in this particular area. We provide a view of fraud ranking in mobile apps and propose a fraud app detection system. In this system, initially main process is based upon data preprocessing. In which various methods like stop word filtering, stemming, removal of URLs, non-dictionary word removal and also POStagging (Parts Of Speech tagging) are included. There are three types of evidences, 1) ranking based evidences 2) rating based evidences and 3) review based evidences. We propose an evidence aggregation method to integrate all the evidences for fraud detection. Finally, we show the apps which are fraud or genuine based on the polarity of the reviews.

Extracting problematic API features from forum discussions

2013 21st International Conference on Program Comprehension (ICPC), 2013

Software engineering activities often produce large amounts of unstructured data. Useful information can be extracted from such data to facilitate software development activities, such as bug reports management and documentation provision. Online forums, in particular, contain extensive valuable information that can aid in software development. However, no work has been done to extract problematic API features from online forums. In this paper, we investigate ways to extract problematic API features that are discussed as a source of difficulty in each thread, using natural language processing and sentiment analysis techniques. Based on a preliminary manual analysis of the content of a discussion thread and a categorization of the role of each sentence therein, we decide to focus on a negative sentiment sentence and its close neighbors as a unit for extracting API features. We evaluate a set of candidate solutions by comparing tool-extracted problematic API design features with manually produced golden test data. Our best solution yields a precision of 89%. We have also investigated three potential applications for our feature extraction solution: (i) highlighting the negative sentence and its neighbors to help illustrate the main API feature; (ii) searching helpful online information using the extracted API feature as a query; (iii) summarizing the problematic features to reveal the "hot topics" in a forum.

Fraud Apps Detection Using Sentiment Analysis and Spam Filtering

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

In the mobile app industry, ranking fraud is the practice of engaging in dishonest or deceitful behavior with the intention of artificially boosting an App's position on a popularity list. In fact, ranking fraud by app developers is becoming more and more common. These practices include inflating their apps' sales or uploading fake app reviews. Although the significance of preventing ranking fraud has long been understood, little knowledge and research have been done in this field. In order to do this, we present a comprehensive analysis of fraud app detection using sentiment analysis and spam filtering in this study and suggest a system for detecting it in mobile apps.

APPBACS: An Application Behavior Analysis And Classification System

International Journal of Computer Science and Information Technology, 2013

Number and complicacy of malware attack has increased multiple folds in recent times. Informed Internet users generally keep their computer protected but get confused when it comes to execute the untrusted applications. In such cases users may fall prey to malicious applications. There are malware behavior analyzers available but leave report analysis to the user. Common users are not trained to understand and analyze these reports, and generally expect direct recommendation whether to execute this application on their computer. This research paper tries to analyze behavior and help the common users and analysts to quickly classify an application as safe or malicious.

Discovery of Ranking of Fraud for Mobile Apps

The Mobile App is a very popular and well known concept due to the rapid advancement in the mobile technology. Due to the large number of mobile Apps, ranking fraud is the key challenge in front of the mobile App market. Ranking fraud refers to fraudulent or vulnerable activities which have a purpose of bumping up the Apps in the popularity list. While the importance and necessity of preventing ranking fraud has been widely recognized. In the existing system the leading event and leading session of an app is identified from the collected historical records. Then three different types of evidences are collected from the user feedbacks namely ranking based evidence, rating based evidence and review based evidence. These three evidences are aggregated by using evidence aggregation method. In the proposed system additionally, we are proposing two enhancements. Firstly, we are using Approval of scores by the admin to identify the exact reviews and rating scores. Secondly, the fake feedbacks by a same person for pushing up that app on the leader board are restricted. Two different constraints are considered for accepting the feedback given to an application. The first constraint is that an app can be rated only once from a user login and the second is implemented with the aid of IP address that limits the number of user login logged per day. Finally, the proposed system will be evaluated with real-world App data which is to be collected from the App Store for a long time period.