Phishing page detection via learning classifiers from page layout feature
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...
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Format: | Article |
Language: | English |
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SpringerOpen
2019-02-01
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Series: | EURASIP Journal on Wireless Communications and Networking |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13638-019-1361-0 |
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author | Jian Mao Jingdong Bian Wenqian Tian Shishi Zhu Tao Wei Aili Li Zhenkai Liang |
author_facet | Jian Mao Jingdong Bian Wenqian Tian Shishi Zhu Tao Wei Aili Li Zhenkai Liang |
author_sort | Jian Mao |
collection | DOAJ |
description | 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. |
first_indexed | 2024-12-13T09:01:34Z |
format | Article |
id | doaj.art-40c89f1f2373464bb0cbc620c460814e |
institution | Directory Open Access Journal |
issn | 1687-1499 |
language | English |
last_indexed | 2024-12-13T09:01:34Z |
publishDate | 2019-02-01 |
publisher | SpringerOpen |
record_format | Article |
series | EURASIP Journal on Wireless Communications and Networking |
spelling | doaj.art-40c89f1f2373464bb0cbc620c460814e2022-12-21T23:53:09ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992019-02-012019111410.1186/s13638-019-1361-0Phishing page detection via learning classifiers from page layout featureJian Mao0Jingdong Bian1Wenqian Tian2Shishi Zhu3Tao Wei4Aili Li5Zhenkai Liang6School of Cyber Science and Technology, Beihang UniversitySchool of Cyber Science and Technology, Beihang UniversitySchool of Cyber Science and Technology, Beihang UniversitySchool of Cyber Science and Technology, Beihang UniversityBaidu USA LLC, Bordeaux DriveInformation Technology Service Center, China National Petroleum CorporationSchool of Computing, National University of SingaporeAbstract 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.http://link.springer.com/article/10.1186/s13638-019-1361-0Anti-phishingMachine learningAggregation analysis |
spellingShingle | Jian Mao Jingdong Bian Wenqian Tian Shishi Zhu Tao Wei Aili Li Zhenkai Liang Phishing page detection via learning classifiers from page layout feature EURASIP Journal on Wireless Communications and Networking Anti-phishing Machine learning Aggregation analysis |
title | Phishing page detection via learning classifiers from page layout feature |
title_full | Phishing page detection via learning classifiers from page layout feature |
title_fullStr | Phishing page detection via learning classifiers from page layout feature |
title_full_unstemmed | Phishing page detection via learning classifiers from page layout feature |
title_short | Phishing page detection via learning classifiers from page layout feature |
title_sort | phishing page detection via learning classifiers from page layout feature |
topic | Anti-phishing Machine learning Aggregation analysis |
url | http://link.springer.com/article/10.1186/s13638-019-1361-0 |
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