Phishing URL Detection Using Machine Learning Classification Algorithms
Abstract
Phishing attack is utilized to get the data like username, secret phrase, financial balance subtleties, and credit card details. Today, is the most well-known cybercrime. Phishing assaults additionally influence the web-based installment area monetary organization, document facilitating or distributed storage, and numerous others. Phishing assault generally focuses to these Web locales which are connected with the internet-based payment area and Web mail. To stop phishing attacks, a variety of methods are employed, including blacklists, heuristics, and visual similarity. The proposed model in this research, however, is a combination of logistic regression (LR) and decision tree (DT) with some variable parameters related to the design and training of classifiers used by data mining techniques related to real-world issues using Python programming language (Spyder IDE) used in developing the model with Sklearn built-in data source library. The system was successfully tested according to the design specification using logistic regression and Decision Tree models. This resulted to 86.33 and 87.62% accuracy level in comparison with the existing system that has 81.42% accuracy rate.
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