DETECTING PHISHING WEBSITE USING MACHINE LEARNING

DETECTING PHISHING WEBSITE USING MACHINE LEARNING

Pakala Shireesha1,Vuturi SaiRani2 ,Thirunahari Venkata Narashimha Charyulu3,Pothapa Anirudh4, Dr.D.S Sameena Begum5

 1,2,3,4B.Tech. Student, Department of Computer Science and Engineering,

shireeshapakala22@gmail.com,vuturisairani@gmail.com, tnarasimha444@gmail.com,

pothapaanirudh8@gmail.com, shashasameena@gmail.com

5Associate Professor, Department of Computer Science and Engineering,

Nalla Malla Reddy Engineering College, Hyderabad, India

Abstract-Phishing attacks continue to evolve and pose significant security risks to online users. The proposed approach incorporates a range of features, including URL, domain, page content, user behavior, and network traffic, to build a classifier model. The model is trained on a large dataset of known phishing and legitimate websites and evaluated on a separate test set to assess its performance. The study also examines the impact of different feature combinations and model selection on the performance of the proposed approach. The experimental results demonstrate that the proposed approach achieves high accuracy, precision, and recall in detecting phishing websites. The research also compares the proposed approach with existing state-of-the-art techniques and highlights its superiority in terms of detection rates. The findings suggest that the proposed approach has practical implications for improving online security and protecting users from phishing attacks.

Keywords-Phishing,Legitimate, MachineLearning, Detection,URL,Accuracy,Precision, Recall.