Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Trustwatch: Innovations In Fraudulent App Detection

Authors
B. Sunayana1, *, Motiki Kavya1, Reddi Pavan Koushik1, Varanasi Shilpa1
1Anil Neerukonda Institute of Technology and Sciences, Sangivalasa, Visakhapatnam, Andhra Pradesh, India
*Corresponding author. Email: sunayana.it@anits.edu.in
Corresponding Author
B. Sunayana
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_73How to use a DOI?
Keywords
Supervised Machine Learning; Detection success rate; PCA; Random forest
Abstract

A brand-new supervised machine learning method is created to categorise network fraud applications as either benign or malevolent. It has been discovered that a combination of feature selection and supervised learning algorithms should be employed to determine the optimal model when taking the detection success rate into account. This study also shows that when it comes to identifying network fraud applications, Random Forest-based machine learning with wrapper feature selection performs better than the support vector machine (PCA) technique. Data gathering, feature extraction, ML model construction, integration of real-time detection, and an intuitive user interface are the essential components. The network fraud program is classified using SVM and RANDOM FOREST supervised machine learning algorithms on the NSL-KDD dataset in order to assess this performance. According to analysis, the suggested approach outperforms other models currently in use in terms of the success rate of fraud application detection.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_73How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - B. Sunayana
AU  - Motiki Kavya
AU  - Reddi Pavan Koushik
AU  - Varanasi Shilpa
PY  - 2025
DA  - 2025/11/04
TI  - Trustwatch: Innovations In Fraudulent App Detection
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 861
EP  - 874
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_73
DO  - 10.2991/978-94-6463-858-5_73
ID  - Sunayana2025
ER  -