Overview of methods for detecting distributed denial-of-service attacks based on machine learning and deep learning
Abstract
Distributed denial of service (DDoS) attacks pose a serious threat to network security. In a Denial of Service (DOS) attack, a single source performs the attack, while DDoS uses multiple hosts to attack the system. It is very difficult to identify the source of the attack when such an attack occurs, since the attacker hides his identity by spoofing his IP address. How to detect DDoS attacks and defend against them is currently an urgent topic both in industry and in scientific circles. This article discusses the mechanism of DDoS attacks and DDoS attack models, the main methods of launching DDoS attacks, types of attacks according to the OSI model and a more detailed description of the types of DDoS attacks aimed at a specific vulnerability. This article systematizes the methods of machine and deep learning used to detect DDoS attacks. In addition to describing the methods themselves, examples of studies where these methods were used to detect DDoS attacks are also given. At the end of the article, examples of environments vulnerable to DDoS attacks are given. This article will help you get acquainted with modern effective methods of detecting DDoS attacks.
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