IMPLEMENTATION OF A SERVER PROTECTION SYSTEM TAKING INTO ACCOUNT ANOMALIES IN PACKAGES

Abstract

The article discusses methods of real-time network traffic analysis based on statistical methods and machine learning algorithms for classifying network packets by their behavioral characteristics. The presented system implements a multi-level approach to server protection, which includes three main stages: primary data filtering, statistical analysis, and the use of machine learning models. The relevance of this issue stems from the need to ensure real-time server protection, which requires high-speed traffic analysis and system adaptability to emerging threats. Modern solutions must not only detect known threats but also identify new, previously unknown attack patterns by analyzing traffic behavioral characteristics. The early anomaly detection module is a key component of the system, enabling the identification of potentially malicious actions at an early stage. To counter new, previously unknown types of attacks, the use of deep neural networks and clustering algorithms is particularly important, as it allows real-time analysis of traffic behavior patterns. The ability to respond to threats before they can cause harm to the infrastructure ensures effective early detection. The presented models allow adapting to new types of attacks by automatically updating them. This makes it possible to detect both traditional DDoS attacks (port scanning, exploitation of network protocol vulnerabilities and SQL injection attempts) and other types of threats. The integration of the presented protection system with existing monitoring tools and firewalls will ensure the accuracy of early detection of DDoS attacks, low false-positive rates, and reliable real-time protection of servers and ease of implementation. Future development prospects for the system include enhancing machine learning algorithms for precise anomaly detection, expanding the functionality of filtering modules, and integrating with cloud technologies to ensure the protection of scalable infrastructures.