9th International Conference on ICT for Sustainable Development

Authors - Rahul Dhaigude, Ruby Chanda
Abstract - Phishing, a prevalent cyber-criminal activity, poses significant threats to individuals and organizations by luring users into disclosing sensitive information through deceptive means. This research aims to address the challenges of phishing website detection by conducting a comprehensive comparative analysis of various data mining techniques and feature selection methods. Furthermore, the study proposes the development of a multi-classifier integration model to bolster the detection process and mitigate the risk posed by phishing URLs. By leveraging multiple classification techniques, including feature selection, the proposed model seeks to enhance the accuracy and efficiency of identifying malicious websites. The ultimate goal is to identify the most suitable algorithm for classification, thereby fortifying defenses against phishing attacks and safeguarding critical data in an increasingly internet-driven society.