Two-stage classification model to detect malicious web pages

Le, Van Lam, Welch, I., Gao, Xiaoying Sharon and Komisarczuk, Peter (2011) Two-stage classification model to detect malicious web pages. In: International Conference on Advanced Information Networking and Applications (AINA), 22-25 March 2011, Biopolis, Singapore.

Full text not available from this repository.

Abstract

Malicious web pages are an emerging security
concern on the Internet due to their popularity and their
potential serious impacts. Detecting and analyzing them is very
costly because of their qualities and complexities. There has
been some research approaches carried out in order to detect
them. The approaches can be classified into two main groups
based on their used analysis features: static feature based and
run-time feature based approaches. While static feature based
approach shows it strengthens as light-weight system, run-time
feature based approach has better performance in term of
detection accuracy. This paper presents a novel two-stage
classification model to detect malicious web pages. Our
approach divided detection process into two stages: Estimating
maliciousness of web pages and then identifying malicious web
pages. Static features are light-weight but less valuable so they
are used to identify potential malicious web pages in the first
stage. Only potential malicious web pages are forwarded to the
second stage for further investigation. On the other hand, runtime
features are costly but more valuable so they are used in
the final stage to identify malicious web pages.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computing
Depositing User: Vani Aul
Date Deposited: 21 Mar 2014 15:08
Last Modified: 10 Dec 2015 16:10
URI: http://repository.uwl.ac.uk/id/eprint/776

Actions (login required)

View Item View Item

Menu