A systematic review: Detecting phishing websites using data mining models
As internet technology use is on the rise globally, phishing constitutes a considerable share of the threats that may attack individuals and organizations, leading to significant losses from personal and confidential information to substantial financial losses. Thus, much research has been dedicated...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-12-01
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| Series: | Intelligent and Converged Networks |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/ICN.2023.0027 |
| _version_ | 1797293812979073024 |
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| author | Dina Jibat Sarah Jamjoom Qasem Abu Al-Haija Abdallah Qusef |
| author_facet | Dina Jibat Sarah Jamjoom Qasem Abu Al-Haija Abdallah Qusef |
| author_sort | Dina Jibat |
| collection | DOAJ |
| description | As internet technology use is on the rise globally, phishing constitutes a considerable share of the threats that may attack individuals and organizations, leading to significant losses from personal and confidential information to substantial financial losses. Thus, much research has been dedicated in recent years to developing effective and robust mechanisms to enhance the ability to trace illegitimate web pages and to distinguish them from non-phishing sites as accurately as possible. Aiming to conclude whether a universally accepted model can detect phishing attempts with 100% accuracy, we conduct a systematic review of research carried out in 2018–2021 published in well-known journals published by Elsevier, IEEE, Springer, and Emerald. Those researchers studied different Data Mining (DM) algorithms, some of which created a whole new model, while others compared the performance of several algorithms. Some studies combined two or more algorithms to enhance the detection performance. Results reveal that while most algorithms achieve accuracies higher than 90%, only some specific models can achieve 100% accurate results. |
| first_indexed | 2024-03-07T21:19:09Z |
| format | Article |
| id | doaj.art-bd3a7fea0e7e4491bbeec534af91c152 |
| institution | Directory Open Access Journal |
| issn | 2708-6240 |
| language | English |
| last_indexed | 2024-03-07T21:19:09Z |
| publishDate | 2023-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Intelligent and Converged Networks |
| spelling | doaj.art-bd3a7fea0e7e4491bbeec534af91c1522024-02-27T14:57:28ZengTsinghua University PressIntelligent and Converged Networks2708-62402023-12-014432634110.23919/ICN.2023.0027A systematic review: Detecting phishing websites using data mining modelsDina Jibat0Sarah Jamjoom1Qasem Abu Al-Haija2Abdallah Qusef3Department of Business Intelligence Technology, Princess Sumaya University for Technology, Amman 11941, JordanDepartment of Business Intelligence Technology, Princess Sumaya University for Technology, Amman 11941, JordanDepartment of Cybersecurity, Princess Sumaya University for Technology, Amman 11941, JordanDepartment of Software Engineering, Princess Sumaya University for Technology, Amman 11941, JordanAs internet technology use is on the rise globally, phishing constitutes a considerable share of the threats that may attack individuals and organizations, leading to significant losses from personal and confidential information to substantial financial losses. Thus, much research has been dedicated in recent years to developing effective and robust mechanisms to enhance the ability to trace illegitimate web pages and to distinguish them from non-phishing sites as accurately as possible. Aiming to conclude whether a universally accepted model can detect phishing attempts with 100% accuracy, we conduct a systematic review of research carried out in 2018–2021 published in well-known journals published by Elsevier, IEEE, Springer, and Emerald. Those researchers studied different Data Mining (DM) algorithms, some of which created a whole new model, while others compared the performance of several algorithms. Some studies combined two or more algorithms to enhance the detection performance. Results reveal that while most algorithms achieve accuracies higher than 90%, only some specific models can achieve 100% accurate results.https://www.sciopen.com/article/10.23919/ICN.2023.0027phishingdata miningmachine learningalgorithmclassification |
| spellingShingle | Dina Jibat Sarah Jamjoom Qasem Abu Al-Haija Abdallah Qusef A systematic review: Detecting phishing websites using data mining models Intelligent and Converged Networks phishing data mining machine learning algorithm classification |
| title | A systematic review: Detecting phishing websites using data mining models |
| title_full | A systematic review: Detecting phishing websites using data mining models |
| title_fullStr | A systematic review: Detecting phishing websites using data mining models |
| title_full_unstemmed | A systematic review: Detecting phishing websites using data mining models |
| title_short | A systematic review: Detecting phishing websites using data mining models |
| title_sort | systematic review detecting phishing websites using data mining models |
| topic | phishing data mining machine learning algorithm classification |
| url | https://www.sciopen.com/article/10.23919/ICN.2023.0027 |
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