Phishing attacks are a serious risk to people and businesses because they try to trick users into sending critical information by using phony emails and websites. Users remain susceptible to these assaults because sophisticated phishing websites are frequently difficult for existing preventive measures to identify and block. As a result, a reliable detection system that can recognize and stop phishing assaults is required. The goal of the research is to create a detection system that efficiently detects and prevents phishing attempts by utilizing online and email isolation techniques to effectively identify and prevent phishing attacks. The detection system consists of three modules: information retrieval, natural language processing, and machine learning. The study adopts an object-oriented analysis and design methodology with a prototyping software development methodology. The developed system provides a comprehensive approach to accurately identifying and preventing phishing attacks by combining web and email isolation techniques with information retrieval, natural language processing, and machine learning.
Clive, A., Akazue, M , Ahweyevu, K & Ogeh, C (2024). Development of a Real-time Phishing Detection Website via a Triumvirate of Information Retrieval, Natural Language Processing. and Machine Learning Modules. Afribary. Retrieved from https://track.afribary.com/works/development-of-a-real-time-phishing-detection-website-via-a-triumvirate-of-information-retrieval-natural-language-processing-and-machine-learning-modules
Clive, Asuai, et. al. "Development of a Real-time Phishing Detection Website via a Triumvirate of Information Retrieval, Natural Language Processing. and Machine Learning Modules" Afribary. Afribary, 07 Feb. 2024, https://track.afribary.com/works/development-of-a-real-time-phishing-detection-website-via-a-triumvirate-of-information-retrieval-natural-language-processing-and-machine-learning-modules. Accessed 05 Nov. 2024.
Clive, Asuai, Maureen Akazue , Kingsley Ahweyevu and Clement Ogeh . "Development of a Real-time Phishing Detection Website via a Triumvirate of Information Retrieval, Natural Language Processing. and Machine Learning Modules". Afribary, Afribary, 07 Feb. 2024. Web. 05 Nov. 2024. < https://track.afribary.com/works/development-of-a-real-time-phishing-detection-website-via-a-triumvirate-of-information-retrieval-natural-language-processing-and-machine-learning-modules >.
Clive, Asuai, Maureen Akazue , Kingsley Ahweyevu and Clement Ogeh . "Development of a Real-time Phishing Detection Website via a Triumvirate of Information Retrieval, Natural Language Processing. and Machine Learning Modules" Afribary (2024). Accessed November 05, 2024. https://track.afribary.com/works/development-of-a-real-time-phishing-detection-website-via-a-triumvirate-of-information-retrieval-natural-language-processing-and-machine-learning-modules