DEVELOPMENT OF AN EMAIL PHISHING DETECTION SYSTEM USING MACHINE LEARNING ALGORITHM

This project describes the design and implementation of a machine learning-based phishing email detection system. It begins with an introduction that provides background information about the growing threat of phishing attacks and the need for robust email security mechanisms. A comprehensive literature review analyzes previous research on phishing detection using various techniques such as visual cues, machine learning, deep learning, and heuristics. The strengths and limitations of these approaches are highlighted. This methodology describes a systematic development process that includes object-oriented analysis and design, microservices architecture, and an optimized database schema. Various algorithms such as CNN, RNN and BERT were evaluated, and a hybrid model of BERT + RNN achieved the highest accuracy of 99.61%. We will discuss the tools and technologies used, including Python libraries such as SciKit-Learn and TensorFlow. We will explain how to deploy it on platforms such as Google Cloud and Flask. View workflows, data collection with Kaggle, results, and performance analysis. Finally, we summarize the effectiveness of machine learning, especially hybrid deep learning models, in identifying phishing emails. Key recommendations highlight large datasets, ensemble techniques, hyperparameter tuning, and real-time analysis to further improve this approach. Overall, this paper provides a comprehensive overview of the development of automated phishing detection systems using state-of-the-art techniques.