Research on Network Security Filtering Model and Key Algorithms Based on Network Abnormal Traffic Analysis

Abstract

With the rapid development of network communication technology, the continuous deepening of Internet application and the increasing enrichment of information, the Internet has become an important infrastructure of human society. With the development of network technology and the increasing complexity of network topology, the supervision of network is facing great challenges. Among them, network traffic anomaly detection is one of the important tasks in network supervision. It is an indispensable technical means in network intrusion detection, security monitoring, operation and maintenance. Network security has become a problem faced by people. Anomaly detection is valued by people in academia and industry because it can detect unknown attacks. Researchers have proposed a large number of anomaly detection methods and systems. However, with the continuous growth of network bandwidth and the continuous progress of network attack and defense game, the network itself is in the process of dynamic evolution, and the means and methods of network attack are also evolving, resulting in the improvement of detection accuracy and accuracy of anomaly detection system Operational efficiency, security and ease of use are facing severe challenges. It is of great significance to propose anomaly detection methods with high detection accuracy and high operation efficiency and optimize existing detection algorithms. It is also a cutting-edge scientific problem of common concern in the field of global network security in academia and industry. Based on the prediction model and classifier, this paper proposes a real-time online anomaly detection method for network traffic, and systematically implements this method.