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Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network
Neural network creates a neuron-based network similar to the human nervous system to solve classification problems efficiently. The smishing problem is a binary classification problem in which attackers target smartphone users through text messages. As smishing is a remarkable cybersecurity issue th...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923090/ https://www.ncbi.nlm.nih.gov/pubmed/35308803 http://dx.doi.org/10.1007/s42979-022-01078-0 |
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author | Mishra, Sandhya Soni, Devpriya |
author_facet | Mishra, Sandhya Soni, Devpriya |
author_sort | Mishra, Sandhya |
collection | PubMed |
description | Neural network creates a neuron-based network similar to the human nervous system to solve classification problems efficiently. The smishing problem is a binary classification problem in which attackers target smartphone users through text messages. As smishing is a remarkable cybersecurity issue that is troubling researchers and smartphone users these days. Addressing this security issue using the most efficient algorithm is the need of the hour. This manuscript presented an algorithm for the model proposed by authors in ‘Smishing Detector’ model and implemented it using Neural Network. The result obtained proves that the neural network is much efficient in detecting smishing problem. Neural Network outperformed other machine learning algorithms with a difference of 1.11%. Neural Network performed with the final accuracy of 97.40%. In this paper, system extracted the most efficient features of smishing SMS (Short Message Service) using the Neural Network. This manuscript also reported the accuracy shown by the system for each feature selected and implemented. It is evident from the implementation that each feature selected is most effective in smishing detection and URL (Uniform Resource Locator) feature is the most effective feature with an accuracy of 94%. |
format | Online Article Text |
id | pubmed-8923090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-89230902022-03-15 Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network Mishra, Sandhya Soni, Devpriya SN Comput Sci Original Research Neural network creates a neuron-based network similar to the human nervous system to solve classification problems efficiently. The smishing problem is a binary classification problem in which attackers target smartphone users through text messages. As smishing is a remarkable cybersecurity issue that is troubling researchers and smartphone users these days. Addressing this security issue using the most efficient algorithm is the need of the hour. This manuscript presented an algorithm for the model proposed by authors in ‘Smishing Detector’ model and implemented it using Neural Network. The result obtained proves that the neural network is much efficient in detecting smishing problem. Neural Network outperformed other machine learning algorithms with a difference of 1.11%. Neural Network performed with the final accuracy of 97.40%. In this paper, system extracted the most efficient features of smishing SMS (Short Message Service) using the Neural Network. This manuscript also reported the accuracy shown by the system for each feature selected and implemented. It is evident from the implementation that each feature selected is most effective in smishing detection and URL (Uniform Resource Locator) feature is the most effective feature with an accuracy of 94%. Springer Nature Singapore 2022-03-15 2022 /pmc/articles/PMC8923090/ /pubmed/35308803 http://dx.doi.org/10.1007/s42979-022-01078-0 Text en © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Mishra, Sandhya Soni, Devpriya Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network |
title | Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network |
title_full | Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network |
title_fullStr | Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network |
title_full_unstemmed | Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network |
title_short | Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network |
title_sort | implementation of ‘smishing detector’: an efficient model for smishing detection using neural network |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923090/ https://www.ncbi.nlm.nih.gov/pubmed/35308803 http://dx.doi.org/10.1007/s42979-022-01078-0 |
work_keys_str_mv | AT mishrasandhya implementationofsmishingdetectoranefficientmodelforsmishingdetectionusingneuralnetwork AT sonidevpriya implementationofsmishingdetectoranefficientmodelforsmishingdetectionusingneuralnetwork |