Teaching Management and Monitoring Abnormal Network Behaviors Under COVID-19

Abstract

Due to the epidemic of COVID-19, more social activities have been moved to the internet, such as online education and online learning. The education management to avoid burst events is a basic requirement of online education, especially when a huge number of persons are visiting at the same time. In order to monitor the abnormal and burst access in online education systems, this paper proposes an anomaly detection method by using data flow to mining high frequency events among massive network traffic data during online education. First, the data flow in traffic network is described as a special structure which is used to establish an efficient high frequent event detection algorithm. Second, the network traffic flow is reduced to make it possible to monitor large-scale concurrent network visiting. The effectiveness of the abnormal network behavior detection method is verified through the experiment on a real network environment for online education.