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Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques by Sumitra Das Guptta; Khandaker Tayef Shahriar; Hamed Alqahtani; Dheyaaldin Alsalman; Iqbal H. Sarker is a Mathematics article available to read on EtoBox.

What is Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques about?

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on thirdparty sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity.To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning cl

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Author

Sumitra Das Guptta; Khandaker Tayef Shahriar; Hamed Alqahtani; Dheyaaldin Alsalman; Iqbal H. Sarker

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Mathematics (Physical Sciences)