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In recent years, cyberattacks, including DDoS attacks, on the critical information infrastructure of the Russian Federation have resulted in financial losses for companies, enterprises, individuals, universities and even hospitals. The damage reaches trillions of roubles, and on average, each large online shop that has been attacked can lose up to 600,000 roubles a day. And this is despite the fact that most companies have their own equipment and software to detect and filter DDoS attacks, or use the services of providers/data centres. The main reason is that not all companies, providers and data centres have sufficient capacity to filter DDoS attacks of various types and types. In addition, an equally important reason is the misconfiguration of physical servers and network equipment ranging from switches to software-defined networks (SDNs)/content delivery networks (CDNs). Thus, given the importance and necessity of ensuring the availability of critical information infrastructure in the era of digital economy, this paper presents a comprehensive systematic review of DDoS attack types and their intelligent filtering techniques. The main findings and results of this study open up the possibility of implementing next-generation systems based on neural networks and computational clusters to analyse network traffic and detect DDoS attacks. In addition, these systems will help to solve existing critical problems, the main ones being the speed of response to emerging cyberattacks and the quality of filtering unauthorised network traffic.
E.V. et al. (Fri,) studied this question.