A comparative analysis on artificial intelligence techniques for web phishing classification
Over the last years, the web has beenexpanded to serve millions of users for various purposes all over the world. The web content filtering is essential to filter offensive, unwanted web content from web pages, reduced inappropriate content to prevent access to content which could compromise the net...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/28089/ http://umpir.ump.edu.my/id/eprint/28089/13/A%20Comparative%20Analysis%20on%20Artificial%20Intelligence.pdf |
| _version_ | 1848822963196919808 |
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| author | Tengku Balqis, Tengku Abd Rashid Jamaludin, Sallim Yusnita, Muhamad Noor |
| author_facet | Tengku Balqis, Tengku Abd Rashid Jamaludin, Sallim Yusnita, Muhamad Noor |
| author_sort | Tengku Balqis, Tengku Abd Rashid |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Over the last years, the web has beenexpanded to serve millions of users for various purposes all over the world. The web content filtering is essential to filter offensive, unwanted web content from web pages, reduced inappropriate content to prevent access to content which could compromise the network and spread maIware, It also to tightened network security where web content filtering adds a much-need layer of security to the network by blocking access to sites that raise an alaQ* However, there are lack of comparison between classification techniques in previous studies in order to find the best classifier for the web page classification and the analysis related to it Thus, the purpose of this study was to apply web page classification techniques and their performances is compared it is the initial step in data mining before going to web filtering. In this project, three classifiers called ArCBlial Neural Network, J48 Decision Tree and Support Vector Machine were used to web phishing dataset in order to find the best possible classifier with small computational efforts that will give the best result in classifying the web page. |
| first_indexed | 2025-11-15T02:49:35Z |
| format | Conference or Workshop Item |
| id | ump-28089 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:49:35Z |
| publishDate | 2020 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-280892021-01-18T08:45:41Z http://umpir.ump.edu.my/id/eprint/28089/ A comparative analysis on artificial intelligence techniques for web phishing classification Tengku Balqis, Tengku Abd Rashid Jamaludin, Sallim Yusnita, Muhamad Noor QA76 Computer software Over the last years, the web has beenexpanded to serve millions of users for various purposes all over the world. The web content filtering is essential to filter offensive, unwanted web content from web pages, reduced inappropriate content to prevent access to content which could compromise the network and spread maIware, It also to tightened network security where web content filtering adds a much-need layer of security to the network by blocking access to sites that raise an alaQ* However, there are lack of comparison between classification techniques in previous studies in order to find the best classifier for the web page classification and the analysis related to it Thus, the purpose of this study was to apply web page classification techniques and their performances is compared it is the initial step in data mining before going to web filtering. In this project, three classifiers called ArCBlial Neural Network, J48 Decision Tree and Support Vector Machine were used to web phishing dataset in order to find the best possible classifier with small computational efforts that will give the best result in classifying the web page. IOP Publishing 2020 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/28089/13/A%20Comparative%20Analysis%20on%20Artificial%20Intelligence.pdf Tengku Balqis, Tengku Abd Rashid and Jamaludin, Sallim and Yusnita, Muhamad Noor (2020) A comparative analysis on artificial intelligence techniques for web phishing classification. In: IOP Conference Series: Materials Science and Engineering, The 6th International Conference on Software Engineering & Computer Systems , 25-27 September 2019 , Pahang, Malaysia. pp. 1-14., 769 (012073). ISSN 1757-899X (Published) https://doi.org/10.1088/1757-899X/769/1/012073 |
| spellingShingle | QA76 Computer software Tengku Balqis, Tengku Abd Rashid Jamaludin, Sallim Yusnita, Muhamad Noor A comparative analysis on artificial intelligence techniques for web phishing classification |
| title | A comparative analysis on artificial intelligence techniques for web phishing classification |
| title_full | A comparative analysis on artificial intelligence techniques for web phishing classification |
| title_fullStr | A comparative analysis on artificial intelligence techniques for web phishing classification |
| title_full_unstemmed | A comparative analysis on artificial intelligence techniques for web phishing classification |
| title_short | A comparative analysis on artificial intelligence techniques for web phishing classification |
| title_sort | comparative analysis on artificial intelligence techniques for web phishing classification |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/28089/ http://umpir.ump.edu.my/id/eprint/28089/ http://umpir.ump.edu.my/id/eprint/28089/13/A%20Comparative%20Analysis%20on%20Artificial%20Intelligence.pdf |