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
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Abstract
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.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Uncontrolled Keywords: | Web Page Classification; Artificial Neural Network; SVM (Support Vector Machine); J48 Decision Tree |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Faculty of Computer System And Software Engineering |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 18 Jan 2021 08:45 |
| Last Modified: | 18 Jan 2021 08:45 |
| URI: | http://umpir.ump.edu.my/id/eprint/28089 |
| Download Statistic: | View Download Statistics |
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