Asynchronous Programming based on Services with Application of Neural Networks as a Method of Taking Legitimate Measures at DDoS Attacks

Abstract

The relevance of this study is conditioned by the growing threat of various attacks in the modern information space. The purpose of this study was to analyze and evaluate the effectiveness of applying asynchronous programming and neural networks to combat availability attacks. A rudimentary C# programme was created to simulate a DDoS attack detection system, and a comparative table was generated to assess different DDoS attack countermeasure services. The results illustrate the pragmatic importance of utilizing neural networks and asynchronous programming in detecting DDoS attacks, emphasizing their capacity to enhance the effectiveness, precision, and flexibility of detection systems. Such methods allow for a quick and effective response to attacks and ensure the stability of information systems, reducing the risk of loss of availability and financial losses. The study also highlights the importance of evaluating the scalability and performance of these methods in actual network environments. The practical significance of this study is that it provides new ways and tools to protect information resources from attacks, contributes to the advancement of scientific knowledge and provides certain solutions to combat information threats.