Defending Malicious Script Attacks Using Machine Learning Classifiers
Theweb application has become a primary target for cyber criminals by injecting malware especially JavaScript to performmalicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes...
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| Format: | Article |
| Language: | English |
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Hindawi
2017
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| Online Access: | http://ir.unimas.my/id/eprint/15729/ http://ir.unimas.my/id/eprint/15729/1/Defending%20Malicious%20Script%20Attacks%20Using%20Machine%20%28abstract%29.pdf |
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| author | Nayeem, Khan Johari, Abdullah Adnan, Shahid Khan |
| author_facet | Nayeem, Khan Johari, Abdullah Adnan, Shahid Khan |
| author_sort | Nayeem, Khan |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Theweb application has become a primary target for cyber criminals by injecting malware especially JavaScript to performmalicious
activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious
activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an
interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper
method for dimensionality reduction. Supervisedmachine learning classifiers were used on the dataset for achieving high accuracy.
Experimental results show that our method can efficiently classify malicious code from benign code with promising results. |
| first_indexed | 2025-11-15T06:47:13Z |
| format | Article |
| id | unimas-15729 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:47:13Z |
| publishDate | 2017 |
| publisher | Hindawi |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-157292022-09-29T03:43:12Z http://ir.unimas.my/id/eprint/15729/ Defending Malicious Script Attacks Using Machine Learning Classifiers Nayeem, Khan Johari, Abdullah Adnan, Shahid Khan T Technology (General) Theweb application has become a primary target for cyber criminals by injecting malware especially JavaScript to performmalicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper method for dimensionality reduction. Supervisedmachine learning classifiers were used on the dataset for achieving high accuracy. Experimental results show that our method can efficiently classify malicious code from benign code with promising results. Hindawi 2017 Article PeerReviewed text en http://ir.unimas.my/id/eprint/15729/1/Defending%20Malicious%20Script%20Attacks%20Using%20Machine%20%28abstract%29.pdf Nayeem, Khan and Johari, Abdullah and Adnan, Shahid Khan (2017) Defending Malicious Script Attacks Using Machine Learning Classifiers. Wireless Communications and Mobile Computing, 2017. ISSN 1530-8677 https://www.hindawi.com/journals/wcmc/ DOI: 10.1155/2017/5360472 |
| spellingShingle | T Technology (General) Nayeem, Khan Johari, Abdullah Adnan, Shahid Khan Defending Malicious Script Attacks Using Machine Learning Classifiers |
| title | Defending Malicious Script Attacks Using Machine
Learning Classifiers |
| title_full | Defending Malicious Script Attacks Using Machine
Learning Classifiers |
| title_fullStr | Defending Malicious Script Attacks Using Machine
Learning Classifiers |
| title_full_unstemmed | Defending Malicious Script Attacks Using Machine
Learning Classifiers |
| title_short | Defending Malicious Script Attacks Using Machine
Learning Classifiers |
| title_sort | defending malicious script attacks using machine
learning classifiers |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/15729/ http://ir.unimas.my/id/eprint/15729/ http://ir.unimas.my/id/eprint/15729/ http://ir.unimas.my/id/eprint/15729/1/Defending%20Malicious%20Script%20Attacks%20Using%20Machine%20%28abstract%29.pdf |