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

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