Fraudulent e-Commerce website detection model using HTML, text and image features
Many of Internet users have been the victims of fraudulent e-commerce websites and the number grows. This paper presents an investigation on three types of features namely HTML tags, textual content and image of the website that could possibly contain some patterns that indicate it is fraudulent. Fo...
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| Format: | Conference or Workshop Item |
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2020
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| _version_ | 1796865766815956992 |
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| author | Khoo, Eric Zainal, Anazida Ariffin, Nurfadilah Kassim, Mohd. Nizam Maarof, Mohd Aizaini Bakhtiari, Majid |
| author_facet | Khoo, Eric Zainal, Anazida Ariffin, Nurfadilah Kassim, Mohd. Nizam Maarof, Mohd Aizaini Bakhtiari, Majid |
| author_sort | Khoo, Eric |
| collection | ePrints |
| description | Many of Internet users have been the victims of fraudulent e-commerce websites and the number grows. This paper presents an investigation on three types of features namely HTML tags, textual content and image of the website that could possibly contain some patterns that indicate it is fraudulent. Four machine learning algorithms were used to measure the accuracy of the fraudulent e-commerce websites detection. These techniques are Linear Regression, Decision Tree, Random Forest and XGBoost. 497 e-commerce websites were used as training and testing dataset. Testing was done in two phases. In phase one, each features was tested to see its discriminative capability. Meanwhile in phase two, these features were combined. The result shows that textual content has consistently outperformed the other two features especially when XGBoost was used as a classifier. With combined features, overall accuracy has improved and best result of accuracy recorded was 98.7% achieved when Linear Regression was used as a classifier. |
| first_indexed | 2024-03-05T21:02:04Z |
| format | Conference or Workshop Item |
| id | utm.eprints-94157 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:02:04Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | utm.eprints-941572022-02-28T13:24:56Z http://eprints.utm.my/94157/ Fraudulent e-Commerce website detection model using HTML, text and image features Khoo, Eric Zainal, Anazida Ariffin, Nurfadilah Kassim, Mohd. Nizam Maarof, Mohd Aizaini Bakhtiari, Majid QA75 Electronic computers. Computer science Many of Internet users have been the victims of fraudulent e-commerce websites and the number grows. This paper presents an investigation on three types of features namely HTML tags, textual content and image of the website that could possibly contain some patterns that indicate it is fraudulent. Four machine learning algorithms were used to measure the accuracy of the fraudulent e-commerce websites detection. These techniques are Linear Regression, Decision Tree, Random Forest and XGBoost. 497 e-commerce websites were used as training and testing dataset. Testing was done in two phases. In phase one, each features was tested to see its discriminative capability. Meanwhile in phase two, these features were combined. The result shows that textual content has consistently outperformed the other two features especially when XGBoost was used as a classifier. With combined features, overall accuracy has improved and best result of accuracy recorded was 98.7% achieved when Linear Regression was used as a classifier. 2020 Conference or Workshop Item PeerReviewed Khoo, Eric and Zainal, Anazida and Ariffin, Nurfadilah and Kassim, Mohd. Nizam and Maarof, Mohd Aizaini and Bakhtiari, Majid (2020) Fraudulent e-Commerce website detection model using HTML, text and image features. In: 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019, 13 – 15 December 2019, Hyderabad, India. http://dx.doi.org/10.1007/978-3-030-49345-5_19 |
| spellingShingle | QA75 Electronic computers. Computer science Khoo, Eric Zainal, Anazida Ariffin, Nurfadilah Kassim, Mohd. Nizam Maarof, Mohd Aizaini Bakhtiari, Majid Fraudulent e-Commerce website detection model using HTML, text and image features |
| title | Fraudulent e-Commerce website detection model using HTML, text and image features |
| title_full | Fraudulent e-Commerce website detection model using HTML, text and image features |
| title_fullStr | Fraudulent e-Commerce website detection model using HTML, text and image features |
| title_full_unstemmed | Fraudulent e-Commerce website detection model using HTML, text and image features |
| title_short | Fraudulent e-Commerce website detection model using HTML, text and image features |
| title_sort | fraudulent e commerce website detection model using html text and image features |
| topic | QA75 Electronic computers. Computer science |
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