Fraud usage detection in internet users based on log data

The Internet has become one of the most important daily social, financial and other activities. The number of customers who use the Internet to conduct their business and purchases is very large. This results in billions of dollars being transferred every day online. Such a large amount of money attracts the attention of cybercriminals to carry out their illegal activities. “Fraud” is one of the most dangerous of these methods, especially phishing, where attackers try to steal user credentials using fraudulent emails, fake websites, or both. The proposed system in this paper includes efficient data extraction from the web file through data collection and preprocessing. and web usage mining procedure to extract features that demonstrate user behavior. an‎d feature-extracting URL analysis to detect website phishing addresses. After that, the features from the above two parts are combined to make the number of features sixty-three. Finally, a classification algorithm (Random Forests) is applied to determine if website addresses are phishing or legitimate. Suggested algorithms performance is determined by using a confusion matrix that shows the robustness of the proposed system.