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Enhong Chen - One of the best experts on this subject based on the ideXlab platform.

  • Discovery of ranking fraud for Mobile Apps
    IEEE Transactions on Knowledge and Data Engineering, 2015
    Co-Authors: Hengshu Zhu, Yong Ge, Hui Xiong, Enhong Chen
    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 validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

  • Popularity Modeling for Mobile Apps: A Sequential Approach
    IEEE Transactions on Cybernetics, 2015
    Co-Authors: Yong Ge, Hui Xiong, Enhong Chen
    Abstract:

    The popularity information in App stores, such as chart rankings, user ratings, and user reviews, provides an unprecedented opportunity to understand user experiences with Mobile Apps, learn the process of adoption of Mobile Apps, and thus enables better Mobile App services. While the importance of popularity information is well recognized in the literature, the use of the popularity information for Mobile App services is still fragmented and under-explored. To this end, in this paper, we propose a sequential approach based on hidden Markov model (HMM) for modeling the popularity information of Mobile Apps toward Mobile App services. Specifically, we first propose a popularity based HMM (PHMM) to model the sequences of the heterogeneous popularity observations of Mobile Apps. Then, we introduce a bipartite based method to precluster the popularity observations. This can help to learn the parameters and initial values of the PHMM efficiently. Furthermore, we demonstrate that the PHMM is a general model and can be applicable for various Mobile App services, such as trend based App recommendation, rating and review spam detection, and ranking fraud detection. Finally, we validate our approach on two real-world data sets collected from the Apple Appstore. Experimental results clearly validate both the effectiveness and efficiency of the proposed popularity modeling approach.

Melissa Vetter - One of the best experts on this subject based on the ideXlab platform.

Yong Ge - One of the best experts on this subject based on the ideXlab platform.

  • Discovery of ranking fraud for Mobile Apps
    IEEE Transactions on Knowledge and Data Engineering, 2015
    Co-Authors: Hengshu Zhu, Yong Ge, Hui Xiong, Enhong Chen
    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 validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

  • Popularity Modeling for Mobile Apps: A Sequential Approach
    IEEE Transactions on Cybernetics, 2015
    Co-Authors: Yong Ge, Hui Xiong, Enhong Chen
    Abstract:

    The popularity information in App stores, such as chart rankings, user ratings, and user reviews, provides an unprecedented opportunity to understand user experiences with Mobile Apps, learn the process of adoption of Mobile Apps, and thus enables better Mobile App services. While the importance of popularity information is well recognized in the literature, the use of the popularity information for Mobile App services is still fragmented and under-explored. To this end, in this paper, we propose a sequential approach based on hidden Markov model (HMM) for modeling the popularity information of Mobile Apps toward Mobile App services. Specifically, we first propose a popularity based HMM (PHMM) to model the sequences of the heterogeneous popularity observations of Mobile Apps. Then, we introduce a bipartite based method to precluster the popularity observations. This can help to learn the parameters and initial values of the PHMM efficiently. Furthermore, we demonstrate that the PHMM is a general model and can be applicable for various Mobile App services, such as trend based App recommendation, rating and review spam detection, and ranking fraud detection. Finally, we validate our approach on two real-world data sets collected from the Apple Appstore. Experimental results clearly validate both the effectiveness and efficiency of the proposed popularity modeling approach.

Hui Xiong - One of the best experts on this subject based on the ideXlab platform.

  • Discovery of ranking fraud for Mobile Apps
    IEEE Transactions on Knowledge and Data Engineering, 2015
    Co-Authors: Hengshu Zhu, Yong Ge, Hui Xiong, Enhong Chen
    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 validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

  • Popularity Modeling for Mobile Apps: A Sequential Approach
    IEEE Transactions on Cybernetics, 2015
    Co-Authors: Yong Ge, Hui Xiong, Enhong Chen
    Abstract:

    The popularity information in App stores, such as chart rankings, user ratings, and user reviews, provides an unprecedented opportunity to understand user experiences with Mobile Apps, learn the process of adoption of Mobile Apps, and thus enables better Mobile App services. While the importance of popularity information is well recognized in the literature, the use of the popularity information for Mobile App services is still fragmented and under-explored. To this end, in this paper, we propose a sequential approach based on hidden Markov model (HMM) for modeling the popularity information of Mobile Apps toward Mobile App services. Specifically, we first propose a popularity based HMM (PHMM) to model the sequences of the heterogeneous popularity observations of Mobile Apps. Then, we introduce a bipartite based method to precluster the popularity observations. This can help to learn the parameters and initial values of the PHMM efficiently. Furthermore, we demonstrate that the PHMM is a general model and can be applicable for various Mobile App services, such as trend based App recommendation, rating and review spam detection, and ranking fraud detection. Finally, we validate our approach on two real-world data sets collected from the Apple Appstore. Experimental results clearly validate both the effectiveness and efficiency of the proposed popularity modeling approach.

Gautam Nagesh Peri - One of the best experts on this subject based on the ideXlab platform.

  • code injection attacks on html5 based Mobile Apps characterization detection and mitigation
    Computer and Communications Security, 2014
    Co-Authors: Xuchao Hu, Kailiang Ying, Wenliang Du, Gautam Nagesh Peri
    Abstract:

    Due to the portability advantage, HTML5-based Mobile Apps are getting more and more popular.Unfortunately, the web technology used by HTML5-based Mobile Apps has a dangerous feature, which allows data and code to be mixed together, making code injection attacks possible. In this paper, we have conducted a systematic study on this risk in HTML5-based Mobile Apps. We found a new form of code injection attack, which inherits the fundamental cause of Cross-Site Scripting attack~(XSS), but it uses many more channels to inject code than XSS. These channels, unique to Mobile devices, include Contact, SMS, Barcode, MP3, etc. To assess the prevalence of the code injection vulnerability in HTML5-based Mobile Apps, we have developed a vulnerability detection tool to analyze 15,510 PhoneGap Apps collected from Google Play. 478 Apps are flagged as vulnerable, with only 2.30\% false-positive rate. We have also implemented a prototype called NoInjection as a Patch to PhoneGap in Android to defend against the attack.

  • Code Injection Attacks on HTML5-based Mobile Apps
    2014
    Co-Authors: Kailiang Ying, Gautam Nagesh Peri, Xuchao Hu, Xing Jin, Wenliang Du, Heng Yin
    Abstract:

    HTML5-based Mobile Apps become more and more popular, mostly because they are much easier to be ported across different Mobile platforms than native Apps. HTML5-based Apps are implemented using the standard web technologies, including HTML5, JavaScript and CSS; they depend on some middlewares, such as PhoneGap, to interact with the underlying OS. Knowing that JavaScript is subject to code injection attacks, we have conducted a systematic study on HTML5-based Mobile Apps, trying to evaluate whether it is safe to rely on the web technologies for Mobile app development. Our discoveries are quite surprising. We found out that if HTML5-based Mobile Apps become popular--it seems to go that direction based on the current projection--many of the things that we normally do today may become dangerous, including reading from 2D barcodes, scanning Wi-Fi access points, playing MP4 videos, pairing with Bluetooth devices, etc. This paper describes how HTML5-based Apps can become vulnerable, how attackers can exploit their vulnerabilities through a variety of channels, and what damage can be achieved by the attackers. In addition to demonstrating the attacks through example Apps, we have studied 186 PhoneGap plugins, used by Apps to achieve a variety of functionalities, and we found that 11 are vulnerable. We also found two real HTML5-based Apps that are vulnerable to the attacks.

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