Please use this identifier to cite or link to this item: https://doi.org/10.1186/s13638-019-1361-0
Title: Phishing page detection via learning classifiers from page layout feature
Authors: Mao, J.
Bian, J.
Tian, W.
Zhu, S.
Wei, T.
Li, A.
Liang, Z. 
Keywords: Aggregation analysis
Anti-phishing
Machine learning
Issue Date: 2019
Publisher: Springer International Publishing
Citation: Mao, J., Bian, J., Tian, W., Zhu, S., Wei, T., Li, A., Liang, Z. (2019). Phishing page detection via learning classifiers from page layout feature. Eurasip Journal on Wireless Communications and Networking 2019 (1) : 43. ScholarBank@NUS Repository. https://doi.org/10.1186/s13638-019-1361-0
Rights: Attribution 4.0 International
Abstract: The web technology has become the cornerstone of a wide range of platforms, such as mobile services and smart Internet-of-things (IoT) systems. In such platforms, users’ data are aggregated to a cloud-based platform, where web applications are used as a key interface to access and configure user data. Securing the web interface requires solutions to deal with threats from both technical vulnerabilities and social factors. Phishing attacks are one of the most commonly exploited vectors in social engineering attacks. The attackers use web pages visually mimicking legitimate web sites, such as banking and government services, to collect users’ sensitive information. Existing phishing defense mechanisms based on URLs or page contents are often evaded by attackers. Recent research has demonstrated that visual layout similarity can be used as a robust basis to detect phishing attacks. In particular, features extracted from CSS layout files can be used to measure page similarity. However, it needs human expertise in specifying how to measure page similarity based on such features. In this paper, we aim to enable automated page-layout-based phishing detection techniques using machine learning techniques. We propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. We prototype our solution and evaluate four popular machine learning classifiers on their accuracy and the factors affecting their results. © 2019, The Author(s).
Source Title: Eurasip Journal on Wireless Communications and Networking
URI: https://scholarbank.nus.edu.sg/handle/10635/213244
ISSN: 1687-1472
DOI: 10.1186/s13638-019-1361-0
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1186_s13638-019-1361-0.pdf2.52 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

28
checked on Oct 1, 2022

Page view(s)

51
checked on Sep 29, 2022

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons