Cargando…

Phishing website prediction using base and ensemble classifier techniques with cross-validation

Internet or public internetwork has become a vulnerable place nowadays as there are so many threats available for the novice or careless users because there exist many types of tools and techniques being used by notorious people on it to victimize people somehow and gain access to their precious and...

Descripción completa

Detalles Bibliográficos
Autores principales: Awasthi, Anjaneya, Goel, Noopur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9628466/
https://www.ncbi.nlm.nih.gov/pubmed/36337366
http://dx.doi.org/10.1186/s42400-022-00126-9
_version_ 1784823199404392448
author Awasthi, Anjaneya
Goel, Noopur
author_facet Awasthi, Anjaneya
Goel, Noopur
author_sort Awasthi, Anjaneya
collection PubMed
description Internet or public internetwork has become a vulnerable place nowadays as there are so many threats available for the novice or careless users because there exist many types of tools and techniques being used by notorious people on it to victimize people somehow and gain access to their precious and personal data resulting in sometimes smaller. However, these victims suffer considerable losses in many instances due to their entrapment in such traps as hacking, cracking, data diddling, Trojan attacks, web jacking, salami attacks, and phishing. Therefore, despite the web users and the software and application developer's continuous effort to make and keep the IT infrastructure safe and secure using many techniques, including encryption, digital signatures, digital certificates, etc. this paper focuses on the problem of phishing to detect and predict phishing websites URLs, primary machine learning classifiers and new ensemble-based techniques are used on 2 distinct datasets. Again on a merged dataset, this study is conducted in 3 phases. First, they include classification using base classifiers, Ensemble classifiers, and then ensemble classifiers are tested with and without cross-validation. Finally, their performance is analyzed, and the results are presented at last to help others use this study for their upcoming research.
format Online
Article
Text
id pubmed-9628466
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Springer Nature Singapore
record_format MEDLINE/PubMed
spelling pubmed-96284662022-11-02 Phishing website prediction using base and ensemble classifier techniques with cross-validation Awasthi, Anjaneya Goel, Noopur Cybersecur (Singap) Research Internet or public internetwork has become a vulnerable place nowadays as there are so many threats available for the novice or careless users because there exist many types of tools and techniques being used by notorious people on it to victimize people somehow and gain access to their precious and personal data resulting in sometimes smaller. However, these victims suffer considerable losses in many instances due to their entrapment in such traps as hacking, cracking, data diddling, Trojan attacks, web jacking, salami attacks, and phishing. Therefore, despite the web users and the software and application developer's continuous effort to make and keep the IT infrastructure safe and secure using many techniques, including encryption, digital signatures, digital certificates, etc. this paper focuses on the problem of phishing to detect and predict phishing websites URLs, primary machine learning classifiers and new ensemble-based techniques are used on 2 distinct datasets. Again on a merged dataset, this study is conducted in 3 phases. First, they include classification using base classifiers, Ensemble classifiers, and then ensemble classifiers are tested with and without cross-validation. Finally, their performance is analyzed, and the results are presented at last to help others use this study for their upcoming research. Springer Nature Singapore 2022-11-02 2022 /pmc/articles/PMC9628466/ /pubmed/36337366 http://dx.doi.org/10.1186/s42400-022-00126-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Awasthi, Anjaneya
Goel, Noopur
Phishing website prediction using base and ensemble classifier techniques with cross-validation
title Phishing website prediction using base and ensemble classifier techniques with cross-validation
title_full Phishing website prediction using base and ensemble classifier techniques with cross-validation
title_fullStr Phishing website prediction using base and ensemble classifier techniques with cross-validation
title_full_unstemmed Phishing website prediction using base and ensemble classifier techniques with cross-validation
title_short Phishing website prediction using base and ensemble classifier techniques with cross-validation
title_sort phishing website prediction using base and ensemble classifier techniques with cross-validation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9628466/
https://www.ncbi.nlm.nih.gov/pubmed/36337366
http://dx.doi.org/10.1186/s42400-022-00126-9
work_keys_str_mv AT awasthianjaneya phishingwebsitepredictionusingbaseandensembleclassifiertechniqueswithcrossvalidation
AT goelnoopur phishingwebsitepredictionusingbaseandensembleclassifiertechniqueswithcrossvalidation