DASH Framework Using Machine Learning Techniques and Security Controls

Abstract

Interest in video streaming has increased recently, as it constitutes most of the traffic on the Internet and cellular networks. These networks use different video streaming technologies. One of the most famous technologies is DASH (which stands for Dynamic Adaptive Steaming using HTTP). DASH adapts streaming parameters according to network conditions and uses the HTTP protocol to communicate between the user and the server. DASH faces many challenges that may lead to video interruptions and poor quality of user experiences (QoE) such as bad network conditions and buffering level control. In addition to the lack of studies, we cover security issues for these types of services. In this paper, we proposed an integrated framework that consists of four components: quality prediction model, precache model, light web application firewall, and a monitoring system. These four components improve QoE and precache and increase the level of security. The results of the quality prediction model are used to predict the quality of the next segments depending on the user’s network conditions and in the precache model to improve caching to reduce the load on the streaming system and rely more on cache servers. The proposed web application firewall is a light version used to defend against video streaming attacks and verify the existence of necessary HTTP headers. The quality predictor model with the generated dataset achieved 97% classification accuracy using DecisionTree, and this experiment proved the strong relationship between congestion periods and streaming quality, which is s the main key in QoE.