Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications
Smartphone app expansion needs strict security measures to avoid fraud and danger. This study overcomes this issue by identifying apps differently. This new solution uses convolutional neural network (CNN), natural language processing (NLP), and the strong AppAuthentix Recommender algorithm to secur...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2024-11-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2515.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846160017878679552 |
|---|---|
| author | Ramnath M. Yesubai Rubavathi C. |
| author_facet | Ramnath M. Yesubai Rubavathi C. |
| author_sort | Ramnath M. |
| collection | DOAJ |
| description | Smartphone app expansion needs strict security measures to avoid fraud and danger. This study overcomes this issue by identifying apps differently. This new solution uses convolutional neural network (CNN), natural language processing (NLP), and the strong AppAuthentix Recommender algorithm to secure app stores and boost customer confidence in the digital marketplace. Since the app ecosystem has grown, counterfeit and harmful applications have risen, threatening consumers and app merchants. These risks need advanced technology that can distinguish malware from legitimate apps. A complex prediction model using CNNs for image analysis, NLP for text feature extraction, and the novel AppAuthentix Recommender algorithm to properly identify legitimate and counterfeit mobile applications is the goal of this research. The whole strategy secures app stores and authenticates apps. The urgent need to safeguard app markets and users against unauthorized and hazardous programs sparked this study. Our cutting-edge solutions make mobile app consumers’ digital lives safer and app marketplaces more trustworthy. CNN, NLP, and AppAuthentix Recommender yielded amazing results in this investigation. Mobile app authenticity may be estimated with 98.25% accuracy. This technology greatly improves app store security and enables mobile app verification. In conclusion, our work offers a novel way to app identification at a time of rapid mobile app development. CNN, NLP, and AppAuthentix Recommender have dramatically enhanced app store security. These new solutions may boost mobile app security and consumer confidence. |
| format | Article |
| id | doaj-art-cd4f6f87d24b443583c9f8f75bffb1ad |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-cd4f6f87d24b443583c9f8f75bffb1ad2024-11-22T15:05:19ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e251510.7717/peerj-cs.2515Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applicationsRamnath M.0Yesubai Rubavathi C.1Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Saveetha Engineering College, Thandalam, Chennai, Tamil Nadu, IndiaSmartphone app expansion needs strict security measures to avoid fraud and danger. This study overcomes this issue by identifying apps differently. This new solution uses convolutional neural network (CNN), natural language processing (NLP), and the strong AppAuthentix Recommender algorithm to secure app stores and boost customer confidence in the digital marketplace. Since the app ecosystem has grown, counterfeit and harmful applications have risen, threatening consumers and app merchants. These risks need advanced technology that can distinguish malware from legitimate apps. A complex prediction model using CNNs for image analysis, NLP for text feature extraction, and the novel AppAuthentix Recommender algorithm to properly identify legitimate and counterfeit mobile applications is the goal of this research. The whole strategy secures app stores and authenticates apps. The urgent need to safeguard app markets and users against unauthorized and hazardous programs sparked this study. Our cutting-edge solutions make mobile app consumers’ digital lives safer and app marketplaces more trustworthy. CNN, NLP, and AppAuthentix Recommender yielded amazing results in this investigation. Mobile app authenticity may be estimated with 98.25% accuracy. This technology greatly improves app store security and enables mobile app verification. In conclusion, our work offers a novel way to app identification at a time of rapid mobile app development. CNN, NLP, and AppAuthentix Recommender have dramatically enhanced app store security. These new solutions may boost mobile app security and consumer confidence.https://peerj.com/articles/cs-2515.pdfAppAuthentix recommenderApp identificationConvolutional Neural Networks (CNN)Counterfeit appsMobile applicationsNatural Language Processing (NLP) |
| spellingShingle | Ramnath M. Yesubai Rubavathi C. Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications PeerJ Computer Science AppAuthentix recommender App identification Convolutional Neural Networks (CNN) Counterfeit apps Mobile applications Natural Language Processing (NLP) |
| title | Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications |
| title_full | Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications |
| title_fullStr | Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications |
| title_full_unstemmed | Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications |
| title_short | Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications |
| title_sort | enhancing appauthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications |
| topic | AppAuthentix recommender App identification Convolutional Neural Networks (CNN) Counterfeit apps Mobile applications Natural Language Processing (NLP) |
| url | https://peerj.com/articles/cs-2515.pdf |
| work_keys_str_mv | AT ramnathm enhancingappauthentixrecommendersystemsusingadvancedmachinelearningtechniquestoidentifygenuineandcounterfeitandroidapplications AT yesubairubavathic enhancingappauthentixrecommendersystemsusingadvancedmachinelearningtechniquestoidentifygenuineandcounterfeitandroidapplications |