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Existing intrusion detection and defense models for CPSS (Cyber-Physical-Social Systems) are based on analyzing the static intrusion characteristics, which cannot effectively detect large-scale Low-Rate Denial-of-Service (LR-DDoS) attacks, especially in the edge environment. In this paper, we firstly explore and enhance Mirai botnet to a sophisticated multi-targets low-rate TCP attack network, which makes edge LR-DDoS more powerful and obfuscates their activity. And then, we develop a novel intrusion detection and defense hybrid method for above CPSS LR-DDoS scenario in edge environment, which takes advantage of locality sensitive features extraction and Deep Convolution Neural Network (DCNN) to auto learn the optimal features of the original data distribution and employs deep reinforcement learning Q-network as the powerful decision maker to defend attacks. The experimental results in detection phase prove the proposed method can distinguish abnormal network attack flows with higher detection accuracy and faster response time than kinds of Support Vector Machines (SVM), K-means and Surface Learning Neural Network etc. Even more, it has a certain detection rate for unknown new attacks, which means the method is effective and suitable for the actual network environment. The experimental results in defense phase prove it can defense LR-DDoS attacks smoothly. INDEX TERMSDeep convolution neural network, Q learning, deep reinforcement learning, edge computing, LR-DDoS, CPSS.

In this paper, an innovative closed hydraulic wind turbine with an energy storage system is proposed. The hydraulic wind turbine consists of the wind rotor, the variable pump, the hydraulic bladder accumulator, the variable motor, and the synchronous generator. The wind energy captured by the wind rotor is converted into hydraulic energy by the variable pump, and then the hydraulic energy is transformed into electrical energy by the variable motor and generator. In order to overcome the fluctuation and intermittence shortcomings of wind power, the hydraulic bladder accumulator is used as an energy storage system in this system to store and release hydraulic energy. A double-loop speed control scheme is presented to allow the wind rotor to operate at optimal aerodynamic performance for different wind speeds and hold the motor speed at the synchronous speed to product constant frequency electrical power regardless of the changes of wind speed and load power. The parameter design and modeling of 600 kW hydraulic wind turbine are accomplished according to the Micon 600 kW wind turbine. Ultimately, time-domain simulations are completed to analyze the dynamic response of the hydraulic wind turbine under the step change conditions of wind speed, rotor speed input, and load power. The simulation results validate the efficiency of the hydraulic wind turbine and speed control scheme presented, moreover, they also show that the systems can achieve the automatic matching among turbine energy, accumulator energy, and generator output energy.