Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis
Public health responses to the COVID-19 pandemic since March 2020 have led to lockdowns and social distancing in most countries around the world, with a shift from the traditional work environment to virtual one. Employees have been encouraged to work from home where possible to slow down the viral...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-03-01
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| Series: | Cybernetics and Information Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/cait-2022-0004 |
| _version_ | 1818013006183792640 |
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| author | Jafar Mousa Tayseer Al-Fawa’reh Mohammad Barhoush Malek Alshira’H Mohammad H. |
| author_facet | Jafar Mousa Tayseer Al-Fawa’reh Mohammad Barhoush Malek Alshira’H Mohammad H. |
| author_sort | Jafar Mousa Tayseer |
| collection | DOAJ |
| description | Public health responses to the COVID-19 pandemic since March 2020 have led to lockdowns and social distancing in most countries around the world, with a shift from the traditional work environment to virtual one. Employees have been encouraged to work from home where possible to slow down the viral infection. The massive increase in the volume of professional activities executed online has posed a new context for cybercrime, with the increase in the number of emails and phishing websites. Phishing attacks have been broadened and extended through years of pandemics COVID-19. This paper presents a novel approach for detecting phishing Uniform Resource Locators (URLs) applying the Gated Recurrent Unit (GRU), a fast and highly accurate phishing classifier system. Comparative analysis of the GRU classification system indicates better accuracy (98.30%) than other classifier systems. |
| first_indexed | 2024-04-14T06:27:07Z |
| format | Article |
| id | doaj.art-7e6b28671494493cbf988f3d1732ab94 |
| institution | Directory Open Access Journal |
| issn | 1314-4081 |
| language | English |
| last_indexed | 2024-04-14T06:27:07Z |
| publishDate | 2022-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Cybernetics and Information Technologies |
| spelling | doaj.art-7e6b28671494493cbf988f3d1732ab942022-12-22T02:07:45ZengSciendoCybernetics and Information Technologies1314-40812022-03-01221607610.2478/cait-2022-0004Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 CrisisJafar Mousa Tayseer0Al-Fawa’reh Mohammad1Barhoush Malek2Alshira’H Mohammad H.3Philadelphia University, Amman, JordanYarmouk University, Irbid, JordanYarmouk University, Irbid, JordanAl al-Bayt University, Mafraq, JordanPublic health responses to the COVID-19 pandemic since March 2020 have led to lockdowns and social distancing in most countries around the world, with a shift from the traditional work environment to virtual one. Employees have been encouraged to work from home where possible to slow down the viral infection. The massive increase in the volume of professional activities executed online has posed a new context for cybercrime, with the increase in the number of emails and phishing websites. Phishing attacks have been broadened and extended through years of pandemics COVID-19. This paper presents a novel approach for detecting phishing Uniform Resource Locators (URLs) applying the Gated Recurrent Unit (GRU), a fast and highly accurate phishing classifier system. Comparative analysis of the GRU classification system indicates better accuracy (98.30%) than other classifier systems.https://doi.org/10.2478/cait-2022-0004cybersecuritycovid-19phishing attackcybercrime |
| spellingShingle | Jafar Mousa Tayseer Al-Fawa’reh Mohammad Barhoush Malek Alshira’H Mohammad H. Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis Cybernetics and Information Technologies cybersecurity covid-19 phishing attack cybercrime |
| title | Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis |
| title_full | Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis |
| title_fullStr | Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis |
| title_full_unstemmed | Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis |
| title_short | Enhancеd Analysis Approach to Detect Phishing Attacks During COVID-19 Crisis |
| title_sort | enhancеd analysis approach to detect phishing attacks during covid 19 crisis |
| topic | cybersecurity covid-19 phishing attack cybercrime |
| url | https://doi.org/10.2478/cait-2022-0004 |
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