DDoS ATTACK DETECTION SYSTEM

The subject matter of this research is the comprehensive analysis and development of an automated network traffic classification system for Distributed Denial of Service (DDoS) attack detection. The study focuses on the transition from traditional signature-based protection paradigms, which are increasingly ineffective against modern, polymorphic, and high-intensity cyber threats –to anomaly-based detection systems powered by artificial intelligence (AI). The goal of the work is to identify effective artificial intelligence algorithms for network traffic analysis and to develop an applied software solution capable of detecting and automatically responding to DDoS attacks to ensure the security of modern information systems. The following tasks were solved in the article: an analysis of modern cyber threats and the limitations of traditional IDS/IPS systems was conducted, highlighting the necessity for adaptive AI-based solutions; a comparative study was performed based on quality metrics for machine learning and deep learning algorithms, specifically: Decision Trees, Random Forest, Support Vector Machines (SVM), and Multilayer Perceptrons (MLP); a program for DDoS attack detection