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Wireless Communications and Mobile Computing
Volume 2017, Article ID 5360472, 9 pages
https://doi.org/10.1155/2017/5360472
Research Article

Defending Malicious Script Attacks Using Machine Learning Classifiers

Department of Computer Systems & Communication Technologies, Faculty of Computer Science & Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia

Correspondence should be addressed to Nayeem Khan; ym.saminu.awsis@94001051

Received 27 October 2016; Accepted 29 December 2016; Published 7 February 2017

Academic Editor: Paul Honeine

Copyright © 2017 Nayeem Khan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The web application has become a primary target for cyber criminals by injecting malware especially JavaScript to perform malicious 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. Supervised machine 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.