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The growth of mobile technology has also been fueled by the extensive use of smartphones, especially Android. With millions of applications, it is now highly important yet challenging to extract useful information. The identification of malicious activity in the market of Android apps and the smart extraction of useful data are the two key subjects of this article. There were over 2.8 million apps in Google Play and over 2.2 million in the Apple App Store as of March 2017. There are half a million or so mobile game apps and over 400,000 developers in the competitive app market. The number of submissions per day is too massive for the classical fraud detection methods like tracing highly rated apps. To drive up ranks, fraudsters use methods such as bot farms and fake review generators. To determine such fraud, this research proposes a system that monitors user reviews and data. Its aim is to enhance the overall reputation of the app store by providing correct and accurate app rankings.

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A Novel Ensemble Learning Framework with PCA for Detecting Fraudulent Rankings in Mobile Applications

  • 会议论文

  • 发表日期：

    2026年

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  • K. Naveen Kumar,
  • M. Dharaniga,
  • M. Kaviyarasan,
  • G. Mohanaprasanth,
  • U. Prakash

摘要

The growth of mobile technology has also been fueled by the extensive use of smartphones, especially Android. With millions of applications, it is now highly important yet challenging to extract useful information. The identification of malicious activity in the market of Android apps and the smart extraction of useful data are the two key subjects of this article. There were over 2.8 million apps in Google Play and over 2.2 million in the Apple App Store as of March 2017. There are half a million or so mobile game apps and over 400,000 developers in the competitive app market. The number of submissions per day is too massive for the classical fraud detection methods like tracing highly rated apps. To drive up ranks, fraudsters use methods such as bot farms and fake review generators. To determine such fraud, this research proposes a system that monitors user reviews and data. Its aim is to enhance the overall reputation of the app store by providing correct and accurate app rankings.