Machine Learning for Detecting the Phishing Threats
Mohammed Ali Shaik, Gangula Rakshitha, Katakam Saipriya, Thakalapally Thrisha, M. Varshini, Jasti Geethika Sai · 2025
In this research, a system is built that detects whether or not a website is getting phished by using machine learning techniques to help enhance cybersecurity and safeguard user data. First, a dataset of legitimate and phishing web sites is collected for training and evaluation of different detection models. Random Forests, Decision Tree and K nearest neighbors (KNN) are used with a discussion on the performance of Random Forests and a suggested hybrid model. The models of the approaches proved more accurate on the task of separating legitimate and fraudulent websites and underscore the importance of the feature extraction and selection strategies for boosting classification results. This thesis highlights the requirement to adapt phishing detection model continuously to mitigate techniques employed by cybercriminals which changes continually. This research is to contribute to the field of cybersecurity by implementing such adaptive systems to infuse internet environments with better security for users.