IMPLEMENTATION OF PROTECTION OF SERVERS WITH ABNORMAL ACCOUNTS IN THE PACKAGE SYSTEM

Abstract

Server protection is a very important aspect of information security as cyber threats grow, especially as network traffic increases and attack complexity. One effective approach is to use a protection system that takes into account network packet anomalies. The detection and processing of such anomalies allows you to quickly identify and neutralize threats where DDos attacks occupy a special place. This article describes how to analyze network traffic in real time based on statistical methods and machine learning algorithms and classify network packets according to their behavioral characteristics [1]. The system implements a multi-layered approach to server protection, which includes three main stages: initial data filtering, statistical analysis, and the use of machine learning models. At the first stage, malicious packets are excluded based on simple criteria, such as forbidden IP addresses and incorrect packet formats.[2] In Phase 2, statistical analysis is used to detect deviations in the traffic distribution, for example, a sharp increase in the number of requests or a change in packet size [3]. The third stage involves the use of classifiers trained with historical data to identify anomalies in network operation. The list of presented models allows you to adapt to new types of attacks by automatically updating [4]. The advantages of the presented system are: It detects both traditional DDoS attacks (port scans, exploits of network protocol vulnerabilities, and SQL injection attempts) and other types of threats. Second, integration with existing monitoring tools and firewalls. Integration with existing monitoring tools and firewalls [5] also makes it easy to implement without significant cost increases. The system is characterized by high attack detection accuracy, low false positive rate. It provides efficient real-time server protection to ensure business continuity and prevent financial and reputation loss.