Abstract
Phishing is one of the most popular and hazardous cybercrime attacks. These attacks are designed to steal information used by people and companies to complete transactions. Phishing websites use a variety of indicators in their text and web browser-based data. This research presents a novel approach to classifying phishing websites by making use of the extreme learning machine (ELM). In this study, SVM, light GBM algorithm was used to detect phishing websites according to characteristics such as the length of their URLs, the number of capital letters they include and the presence of HTML elements. The findings indicate that ELM has a classification accuracy of 94.2% when it comes to phishing websites. This demonstrates the potential of ELM to classify websites that are used for phishing and to improve the safety of users who do their activities online.
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Krishna Reddy, V.V., Sai, Y.N., Keerthi, T., Reddy, K.A. (2024). Detection of Phishing Website Using Support Vector Machine and Light Gradient Boosting Machine Learning Algorithms. In: Hassanien, A.E., Castillo, O., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. ICICC 2023. Lecture Notes in Networks and Systems, vol 731. Springer, Singapore. https://doi.org/10.1007/978-981-99-4071-4_23
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DOI: https://doi.org/10.1007/978-981-99-4071-4_23
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