Detection of Phishing Websites Using Ensemble Machine Learning Approach
In this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrime...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2021-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2021/05/itmconf_icacc2021_03012.pdf |
| _version_ | 1818655100265037824 |
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| author | M. Dharani Badkul Soumya Gharat Kimaya Vidhate Amarsinh Bhosale Dhanashri |
| author_facet | M. Dharani Badkul Soumya Gharat Kimaya Vidhate Amarsinh Bhosale Dhanashri |
| author_sort | M. Dharani |
| collection | DOAJ |
| description | In this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrimes in the modern digital sphere where an attacker imitates an existing - and often trusted - person or entity in an attempt to capture a victim’s login credentials, account information, and other sensitive data. Phishing websites are visually and semantically similar to real ones. The rise in online trading activities has resulted in a rise in the number of phishing scams. Cybersecurity jobs are the most difficult to fill, and the development of an automated system for phishing website detection is the need of the hour. Machine Learning is one of the most feasible methods to approach this situation, as it is capable of handling the dynamic nature of phishing techniques, in addition to providing an accurate method of classification. |
| first_indexed | 2024-12-17T03:04:19Z |
| format | Article |
| id | doaj.art-e7dc6bf9a7ad4ef4933b903704d9687b |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-12-17T03:04:19Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-e7dc6bf9a7ad4ef4933b903704d9687b2022-12-21T22:06:00ZengEDP SciencesITM Web of Conferences2271-20972021-01-01400301210.1051/itmconf/20214003012itmconf_icacc2021_03012Detection of Phishing Websites Using Ensemble Machine Learning ApproachM. Dharani0Badkul Soumya1Gharat Kimaya2Vidhate Amarsinh3Bhosale Dhanashri4Computer Engineering Dept., Ramrao Adik Institute of TechnologyComputer Engineering Dept., Ramrao Adik Institute of TechnologyComputer Engineering Dept., Ramrao Adik Institute of TechnologyComputer Engineering Dept., Ramrao Adik Institute of TechnologyComputer Engineering Dept., Ramrao Adik Institute of TechnologyIn this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrimes in the modern digital sphere where an attacker imitates an existing - and often trusted - person or entity in an attempt to capture a victim’s login credentials, account information, and other sensitive data. Phishing websites are visually and semantically similar to real ones. The rise in online trading activities has resulted in a rise in the number of phishing scams. Cybersecurity jobs are the most difficult to fill, and the development of an automated system for phishing website detection is the need of the hour. Machine Learning is one of the most feasible methods to approach this situation, as it is capable of handling the dynamic nature of phishing techniques, in addition to providing an accurate method of classification.https://www.itm-conferences.org/articles/itmconf/pdf/2021/05/itmconf_icacc2021_03012.pdf |
| spellingShingle | M. Dharani Badkul Soumya Gharat Kimaya Vidhate Amarsinh Bhosale Dhanashri Detection of Phishing Websites Using Ensemble Machine Learning Approach ITM Web of Conferences |
| title | Detection of Phishing Websites Using Ensemble Machine Learning Approach |
| title_full | Detection of Phishing Websites Using Ensemble Machine Learning Approach |
| title_fullStr | Detection of Phishing Websites Using Ensemble Machine Learning Approach |
| title_full_unstemmed | Detection of Phishing Websites Using Ensemble Machine Learning Approach |
| title_short | Detection of Phishing Websites Using Ensemble Machine Learning Approach |
| title_sort | detection of phishing websites using ensemble machine learning approach |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2021/05/itmconf_icacc2021_03012.pdf |
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