IMPLEMENTATION OF A BAYESIAN NETWORK IN PYTHON FOR ANALYSIS OF CYBERCRIMES ASSOCIATED WITH DDOS ATTACKS

The research of cybercrimes, including DDoS attacks, is becoming increasingly important in the context of heightened attention to cybersecurity, protection of information and infrastructure of organizations in the modern world that rely on digital technologies and computer systems. The article argues that the use of Bayesian network models (hereinafter Bayesian networks - BN) for the analysis of cybercrimes (using distributed DDoS attacks as an example) will allow taking into account numerous variables and probabilities. This makes similar research more accurate and reliable. Using the example of BN research in the GeNIe applied software package, the process of using BN apparatus for the cybercrime investigation task related to the implementation of DDoS attacks from an attacker's computer is demonstrated. The described BN helps forensic experts in investigating such cybercrimes to identify motives and connections between attack participants, which undoubtedly improves the efficiency of investigations. The demonstration of BN application using the GeNIe modeling package, as well as the implementation of such BN in the PyCharm IDE environment, emphasizes the potential of Bayesian ne