Defending Malicious Script Attacks Using Machine Learning Classifiers
Joint Authors
Khan, Nayeem
Abdullah, Johari
Khan, Adnan Shahid
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-07
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Khan, Nayeem& Abdullah, Johari& Khan, Adnan Shahid. 2017. Defending Malicious Script Attacks Using Machine Learning Classifiers. Wireless Communications and Mobile Computing،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1205952
Modern Language Association (MLA)
Khan, Nayeem…[et al.]. Defending Malicious Script Attacks Using Machine Learning Classifiers. Wireless Communications and Mobile Computing No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1205952
American Medical Association (AMA)
Khan, Nayeem& Abdullah, Johari& Khan, Adnan Shahid. Defending Malicious Script Attacks Using Machine Learning Classifiers. Wireless Communications and Mobile Computing. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1205952
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1205952