Defending Malicious Script Attacks Using Machine Learning Classifiers
Author:
Affiliation:
1. Department of Computer Systems & Communication Technologies, Faculty of Computer Science & Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia
Abstract
Funder
Universiti Malaysia Sarawak
Publisher
Hindawi Limited
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Link
http://downloads.hindawi.com/journals/wcmc/2017/5360472.pdf
Reference16 articles.
1. Analysis and Identification of Malicious JavaScript Code
2. Desktop and mobile web page comparison: characteristics, trends, and implications
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