A Dual Module Approach for High Accuracy Phishing Detection and Email Prioritization using NLP and Machine Learning

Phishing attacks are a serious and ongoing problem in digital communication, where attackers use weaknesses to get sensitive information. Traditional ways of protecting against these attacks often struggle to keep up with the changing methods used by hackers, which means there is a need for smarter and more flexible solutions. This paper presents a system with two parts designed to make email security better and help people manage their emails more efficiently. The first part uses features from Natural Language Processing and machine learning to decide if an email is phishing or not. The second part looks at emails that are not phishing and assigns them a priority based on their content and situation. To test this system, we used two different sets of data: one standard set used for checking spam and one we created to represent real-life situations. You should specifically state the accuracies here. For instance: "In both cases, the system performed well in identifying phishing emails and setting the right priorities. The phishing detection module achieved an accuracy of 97.97% on the Standard Spam/Ham Dataset and 82.22% on the Custom-Built Dataset, while the email prioritization module achieved 99.71% and 98.89% respectively, even when the data had some errors. These results show that the system is strong and could be a good solution for improving email security and management today."