Abstract: Phishing is a form of fraud in which the attacker tries to learn sensitive information such as login credentials or account information by sending as a reputable entity or person in email or other communication channels. Typically, a victim receives a message that appears to have been sent by a known contact or organization. The message contains malicious software targeting the user’s computer or has links to direct victims to malicious websites in order to trick them into divulging personal and financial information, such as passwords, account IDs or credit card details. The aim of this research is to develop these methods of defense utilizing various approaches to categorize websites. Specifically, we have developed a system that uses machine learning techniques to classify websites based on their URL. We have used two classifiers: The Decision Tree classifier and Naive Bayesian classifier. These classifiers were trained and tested using a dataset that has values of features of 88647 websites. The accuracy of the proposed system is 95.4 % which is higher than most of the other proposed systems.
Keywords: Phishing sites; Machine learning; Classification; Malicious sites; Detection; Cybersecurity;
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DOI:
10.17148/IJARCCE.2021.10734
