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• RESEARCH OF MACHINE LEARNING METHODS FOR DETECTING FRAUDULENT WEBSITES

  М.А. Lapina , D. А. Lukyanov , V.G. Lapin , N.N. Kucherov

  250-262

  2025-10-01

  Abstract ▼

  Every year our lives become more and more connected with large volumes of data that need to be analyzed. As the volume of information increases, its analysis becomes a more voluminous and complex task.
  In this situation, the problem of finding a tool that will help companies and institutions in collecting, analyzing and forecasting data arises. Machine learning is an area of artificial intelligence that finds patterns in a database and, based on them, tries to predict the result. Another area of application of machine learning is the detection of fraudulent sites. Currently, with the development of information technology, digital crimes have become a serious threat to confidential information and user data. Artificial intelligence is able to analyze site parameters and determine the presence of threats to information. The study is aimed at systematizing knowledge about phishing attacks and studying machine learning methods for detecting fraudulent sites. During the study, machine learning methods for detecting phishing sites were developed, schemes were built that allow machine learning models to correctly transform data for feeding them to models. The analysis of the data provided in the dataset made it possible to correctly transform the data for the correct operation of the models, which will avoid errors. The problem of retraining machine learning models was solved. A detailed study of the dataset made it possible to filter out data that could cause errors in the model and reduce the quality of artificial learning forecasting. As a result of the work, the developed methods for searching for phishing attacks using machine learning models were tested on test data, based on the results obtained, graphs of changes in the accuracy of detecting illegitimate sites from changing the model settings were constructed. An analysis of the study was carried out and the results of the work were summarized.

• RESEARCH OF MACHINE LEARNING METHODS FOR DETECTING SPOOFING ATTACKS IN DECENTRALIZED NETWORKS

  М.А. Lapina , R.А. Dymuha , N.N. Kucherov , Е.S. Basan

  16-31

  2025-07-24

  Abstract ▼

  Unmanned aerial vehicles are appearing more and more in our lives and are used for various purposes such as cargo delivery, monitoring, household management, exploration and entertainment. But along with their growing popularity, the number of people who intentionally want to interfere with the operation of UAVs and use them for their own interests and purposes is also increasing. They use various types of attacks to eliminate or intercept the drone by any means. Spoofing attacks are one of the most common and dangerous types of attacks, as they allow attackers to act unnoticed, faking the identifiers of autonomous aircraft or operators, posing as legitimate participants in the system. The purpose of such attacks may be to intercept control, steal data, sabotage, or use UAVs to perform malicious actions such as espionage, damage, or malfunction operations. But every year it becomes more difficult to prevent attacks, as they are difficult to detect and can lead to serious consequences, which is why such a solution as detecting spoofing attacks on an unmanned vehicle using machine learning was invented. The article discusses spoofing attacks on UAVs, analyzes spoofing on autonomous aircraft, and studies machine learning methods for detecting spoofing attacks based on a dataset using the Knime platform. The results of the study demonstrate that the method of detecting attacks using machine learning based on the ensemble method, the Tree Ensemble Learner and Random Forest Learner models, which showed results of 97.110% and 97.039%, respectively, is the best among other methods, which will improve the security of unmanned aerial vehicles, reduce the burden on operators and increase the reliability of the system as a whole. In the future, the proposed approach can be expanded to detect other types of cyberattacks, which will make it a universal method of protection against intruders