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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi
2017
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/15729/1/Defending%20Malicious%20Script%20Attacks%20Using%20Machine%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/15729/ https://www.hindawi.com/journals/wcmc/ |
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| Summary: | 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. |
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