The method of detecting dangerous attacks in the network operation of the flight training department against cyber threats

The aim of the article is to present the results from the analysis of network traffic obtained with the help of the GREYCORTEX MENDEL tool at the Department of Flight Training of the Faculty of Aeronautics TUKE, which serves for advanced analysis of network traffic and detection of hidden malware and anomalies using machine learning and artificial intelligence. The research presents the results of software analysis - GREYCORTEX Mendel for attack detection and prevention of security incidents that could disrupt the stability of the information network of the Department of Flight Training. The article presents a tool to visualize the network traffic of all connected devices of the institution, whose communication is then analyzed and filtered as needed to ensure reliable connection and security. The test operation of this tool was carried out at the Faculty of Aeronautics - Technical University of Kosice (LF TUKE) at the Department of Flight Training. The results of the analysis of network traffic with the help of GREYCORTEX Mendel helped us to discover how the selected network can be attacked. Dangerous threats could be identified and filtered out of network traffic to secure network infrastructure and protect sensitive data.