DETECTION OF PHISHING INTERNET DOMAINS USING MACHINE LEARNING ALGORITHMS IN REAL-TIMEDATA STREAMING

Objective: the aim of this research is to develop an effective method for detecting phishing Internet domains using machine learning algorithms in real-time data streaming. Methodology: the work involves an analysis of features characterizing arbitrary Internet domains, and the development of a software complex that allowed collecting a custom dataset containing a set of features for over 250,000 domains. Several machine learning models were )trained on the obtained dataset and compared in terms of accuracy and speed. The selected classifier was used to develop a software prototype that was tested on a sample of 1,000 arbitrary Internet domains. Results: a classifier and software prototype were developed, enabling the categorization of arbitrary Internet domains as either phishing or legitimate within given accuracy and speed parameters. The validity and justification of the proposed scientific findings, results, and conclusions are supported by a comprehensive review of the current state of the field, systematic justification of the proposed models, which do not contradict known positions of other authors, and a series of experiments confirming the results of theoretical studies. The dataset collected during the work was published on the Kaggle platform for open access and use by researchers for developing various intelligent methods for detecting phishing domains. Scientific novelty: the scientific novelty lies in the development of a method for detecting phishing domains in realtime data streaming, which can be used in intrusion detection systems and in the development of web applications that protect users from unwanted content. A software prototype was implemented and tested based on the obtained model, demonstrating an accuracy of 98.5% with an average processing speed of 1.2 seconds per resource.