Published Paper Details:

This research paper presents the development and evaluation of a novel tool for detecting phishing attempts using Natural Language Processing (NLP) and machine learning techniques. The core objective is to enhance cybersecurity measures by accurately identifying phishing content. The paper details the utilization of a comprehensive technology stack, including Python, Flask for backend development, JavaScript, and React for frontend design, to create an interactive and user-friendly phishing detection tool. The machine learning models employed in this project include Logistic Regression, Decision Tree Classifier, KNeighbors Classifier, and MLP Classifier, each meticulously trained and evaluated using datasets sourced from Kaggle. This paper offers an in-depth analysis of the performance of these models, assessed through metrics such as accuracy, precision, recall, and F1 score. The results demonstrate the effectiveness of the implemented models in phishing detection, providing valuable insights into the application of NLP in the cybersecurity domain. This work represents a significant step forward in the ongoing efforts to combat cybersecurity threats, particularly in the area of phishing detection, through innovative AI and NLP techniques.