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  • Open Access

    ARTICLE

    A Filter-Based Feature Selection Framework to Detect Phishing URLs Using Stacking Ensemble Machine Learning

    Nimra Bari1, Tahir Saleem2, Munam Shah3, Abdulmohsen Algarni4, Asma Patel5,*, Insaf Ullah6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1167-1187, 2025, DOI:10.32604/cmes.2025.070311 - 30 October 2025

    Abstract Today, phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers, passwords, and usernames. We can find several anti-phishing solutions, such as heuristic detection, virtual similarity detection, black and white lists, and machine learning (ML). However, phishing attempts remain a problem, and establishing an effective anti-phishing strategy is a work in progress. Furthermore, while most anti-phishing solutions achieve the highest levels of accuracy on a given dataset, their methods suffer from an increased number of false positives. These methods are ineffective against zero-hour attacks. Phishing sites with… More >

  • Open Access

    ARTICLE

    Phish Block: A Blockchain Framework for Phish Detection in Cloud

    R. N. Karthika*, C. Valliyammai, M. Naveena

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 777-795, 2023, DOI:10.32604/csse.2023.024086 - 01 June 2022

    Abstract The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy. But, deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes. In Spite of much technological advancement, phishing acts as the first step in a series of attacks. With technological advancements, availability and access to the phishing kits has improved drastically, thus making it an ideal tool for the hackers to execute the attacks. The phishing cases indicate use of foreign characters to disguise the original Uniform Resource Locator (URL), typosquatting the popular… More >

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