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The adaptive bit rate (ABR) algorithm has become the focus research in video transmission.However,due to the characteristics of 5G wireless heterogeneous networks,such as large fluctuation of channel bandwidth and obvious differences between different networks,the adaptive video stream transmission with multi-terminal cooperation was faced with great challenges.An adaptive video stream transmission control method based on deep reinforcement learning was proposed.First of all,a video stream dynamic programming model was established to jointly optimize the transmission rate and diversion strategy.Since the solution of this optimization problem depended on accurate channel estimation,dynamically changing channel state was difficult to achieve.Therefore,the dynamic programming problem was improved to reinforcement learning task,and the A3C algorithm was used to dynamically determine the video bit rate and diversion strategy.Finally,the simulation was carried out according to the measured network data,and compared with the traditional optimization method,the method proposed better improved the user QoE.

The adaptive bit rate (ABR) algorithm has become the focus research in video transmission.However,due to the characteristics of 5G wireless heterogeneous networks,such as large fluctuation of channel bandwidth and obvious differences between different networks,the adaptive video stream transmission with multi-terminal cooperation was faced with great challenges.An adaptive video stream transmission control method based on deep reinforcement learning was proposed.First of all,a video stream dynamic programming model was established to jointly optimize the transmission rate and diversion strategy.Since the solution of this optimization problem depended on accurate channel estimation,dynamically changing channel state was difficult to achieve.Therefore,the dynamic programming problem was improved to reinforcement learning task,and the A3C algorithm was used to dynamically determine the video bit rate and diversion strategy.Finally,the simulation was carried out according to the measured network data,and compared with the traditional optimization method,the method proposed better improved the user QoE.

Distributed DNS reflective DDoS attack has become one of the main forms of denial of service attacks，and traditional security technology based on network traffic analysis and network traffic control technology can’t meet the needs of protection.Detection technology of DNS reflection attack based on time to live （TTL）value intelligent judgments was proposed，and the detection technology can accurately detect spoofed source IP address of the packet.The control technology based on multi system fusion can block attack traffic flow into the network in the source.

Distributed DNS reflective DDoS attack has become one of the main forms of denial of service attacks，and traditional security technology based on network traffic analysis and network traffic control technology can’t meet the needs of protection.Detection technology of DNS reflection attack based on time to live （TTL）value intelligent judgments was proposed，and the detection technology can accurately detect spoofed source IP address of the packet.The control technology based on multi system fusion can block attack traffic flow into the network in the source.