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
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Abstract
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.
| Item Type: | Article |
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| Additional Information: | Information, Communication and Creative Technology |
| Uncontrolled Keywords: | Malicious Script, Machine Learning Classifiers, Web applications, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology Faculties, Institutes, Centres > Faculty of Computer Science and Information Technology Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology |
| Depositing User: | Karen Kornalius |
| Date Deposited: | 30 Mar 2017 06:23 |
| Last Modified: | 29 Sep 2022 03:43 |
| URI: | http://ir.unimas.my/id/eprint/15729 |
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