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Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and

Ramnath M1, Yesubai Rubavathi C2

  • 1Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India.

Peerj. Computer Science
|December 9, 2024
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Summary

This study introduces a novel app identification method using Convolutional Neural Networks (CNN) and Natural Language Processing (NLP) to enhance app store security. The approach achieves 98.25% accuracy in detecting fraudulent applications, boosting user confidence.

Keywords:
App identificationAppAuthentix recommenderConvolutional Neural Networks (CNN)Counterfeit appsMobile applicationsNatural Language Processing (NLP)

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • The rapid expansion of the smartphone app ecosystem has led to an increase in counterfeit and malicious applications.
  • Existing security measures are insufficient to effectively distinguish between legitimate and harmful apps, posing risks to consumers and app vendors.
  • There is an urgent need for advanced technological solutions to enhance app store security and user trust.

Purpose of the Study:

  • To develop and evaluate a novel system for authenticating mobile applications and securing app stores.
  • To address the growing threat of fraudulent and harmful apps in the digital marketplace.
  • To improve customer confidence in mobile application platforms.

Main Methods:

  • Utilized Convolutional Neural Networks (CNN) for image analysis of app data.
  • Employed Natural Language Processing (NLP) for extracting features from app-related text.
  • Integrated a novel algorithm, AppAuthentix Recommender, for robust app identification and authentication.

Main Results:

  • The integrated system demonstrated high accuracy in identifying legitimate and counterfeit mobile applications.
  • Achieved an impressive accuracy rate of 98.25% in estimating mobile app authenticity.
  • The developed technology significantly enhances app store security and enables effective mobile app verification.

Conclusions:

  • The study presents a groundbreaking approach to mobile app identification, crucial in the era of rapid app development.
  • The combination of CNN, NLP, and the AppAuthentix Recommender algorithm substantially improves app store security.
  • These advancements contribute to safer mobile app usage and increased consumer trust in digital marketplaces.