Review of Machine Learning Technique based Prediction Model for Phishing Websites Detection

RishiKesh Dube^1.*, Twinkle Sharma², Damodar Tiwari^3,*, Kailash Patidar^3,*

¹Research Scholar, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India

²Assistant Professor, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India

³Professor, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India

Emails: rishikeshdubey6@gmail.com; twinklesharma1311@gmail.com; damodar@bistbpl.in; Kailashpatidar123@gmail.com

Phishing attacks have emerged as a significant cybersecurity challenge, targeting individuals and organizations by tricking users into revealing sensitive information through deceptive websites. Traditional phishing detection methods, such as blacklists and heuristic-based approaches, struggle to keep pace with the rapid evolution of phishing techniques. Machine learning-based predictive models offer a promising solution by analyzing website attributes, URL structures, and behavioral patterns to distinguish between legitimate and phishing websites. This paper provides a comprehensive review of various machine learning techniques, including decision trees, support vector machines (SVM), random forests, deep learning models, and ensemble methods, employed in phishing website detection. It explores feature selection strategies, dataset characteristics, performance evaluation metrics, and real-world implementation challenges. Furthermore, the study discusses recent advancements such as adversarial resilience, natural language processing (NLP) integration, and real-time phishing detection frameworks. The review highlights existing research gaps and future directions to enhance phishing detection accuracy, scalability, and adaptability in evolving cybersecurity landscapes.