HTMTAD: A model to detect anomalies of CDN traffic based on improved HTM network

Abstract

There will always be malicious intrusion, node downtime and other events caused by network traffic anomalies while Content Delivery Network (CDN) is facing user’s service. These events will lead in a large area of network paralysis and suspension of network services. Therefore, in order to effectively detect and deal with the anomalies in advance, the paper makes a partial improvement on the existing Hierarchical Temporal Memory network (HTM), and proposes a new network model HTMTAD (Hierarchical Temporal Memory – based Traffic Anomalies Detection) to detect intelligently the changes of abnormal traffic from the CDN. In view of the characteristics of CDN traffic data, the paper proposes a hash coding algorithm to improve the reliability of encoder and an anomaly likelihood calculation method to detect the CDN traffic anomalies. Experimental results show that HTMTAD can effectively detect anomalies in CDN network traffic.