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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: Nayeem, Khan, Johari, Abdullah, Adnan, Shahid Khan
Format: Article
Language:English
Published: Hindawi 2017
Subjects:
T Technology (General)
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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spelling my.unimas.ir.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
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Nayeem, Khan
Johari, Abdullah
Adnan, Shahid Khan
Defending Malicious Script Attacks Using Machine Learning Classifiers
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.
format Article
author Nayeem, Khan
Johari, Abdullah
Adnan, Shahid Khan
author_facet Nayeem, Khan
Johari, Abdullah
Adnan, Shahid Khan
author_sort Nayeem, Khan
title Defending Malicious Script Attacks Using Machine Learning Classifiers
title_short 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_sort defending malicious script attacks using machine learning classifiers
publisher Hindawi
publishDate 2017
url 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/
_version_ 1745566032090628096
score 13.211869

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