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...
Autores principales: | , |
---|---|
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 |