Defending Malicious Script Attacks Using Machine Learning Classifiers

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

[img]
Preview
PDF
Defending Malicious Script Attacks Using Machine (abstract).pdf

Download (76kB) | Preview
Official URL: https://www.hindawi.com/journals/wcmc/

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
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
Depositing User: Karen Kornalius
Date Deposited: 30 Mar 2017 06:23
Last Modified: 05 Sep 2017 03:07
URI: http://ir.unimas.my/id/eprint/15729

Actions (For repository members only: login required)

View Item View Item