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Integrated risk assessment of complex disaster system based on a non-linear information dynamics model

Science China(Technological Sciences) 2012年第1期55卷 3344-3351页

作者： WANG Wei SU JingYu MA DongHui TIAN Jie Institute of Earthquake Resistance and Disaster Reduction Beijing University of TechnologyBeijing 100124China College of Civil Engineering Beijing University of TechnologyBeijing 100124China

This paper describes a non-linear information dynamics model for integrated risk assessment of complex disaster system from an evolution perspective. According to the occurrence and evolution of natural disaster system with complicated and nonlinear characteristics, a non-linear information dynamics mode is introduced based on the maximum flux principle during modeling process to study the integrated risk assessment of complex disaster system. Based on the non-equilibrium statistical mechanics method, a stochastic evolution equation of this system is established. The integrated risk assessment of complex disaster system can be achieved by giving reasonable weights of each evaluation index to stabilize the system. The new model reveals the formation pattern of risk grade and the dynamics law of evolution. Meanwhile, a method is developed to solve the dynamics evolution equations of complex system through the self-organization feature map algorithm. The proposed method has been used in complex disaster integrated risk assessment for 31 provinces, cities and autonomous regions in China mainland. The results have indicated that the model is objective and effective.

关键词： complex disaster system risk evolution maximum flux principle (MFP) generalized information entropy self-organization feature map (SOFM)

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A DDoS Attack Detection Scheme Based on entropy and PSO-BP Neural Network in SDN

A DDoS Attack Detection Scheme Based on Entropy and PSO-BP N...

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2018中国信息通信大会(CICC2018)

作者： Zheng-Peng Liu Yu-Peng He Wen-Sheng Wang Bin Zhang School of Cyberspace Security and Computer Hebei University information Technology Center Hebei University School of Electronic information Engineering Hebei University

SDN（Software Defined Network）has many security problems,and DDoS attack is undoubtedly the most serious harm to SDN architecture *** to accurately and effectively detect DDoS attacks has always been a difficult point and focus of SDN security *** on the characteristics of SDN,a DDoS attack detection method combining generalized entropy and PSO-BP neural network is *** traffic is pre-detected by the generalized entropy method deployed on the switch,and the detection result is divided into normal and *** the switch that issued the abnormal *** controller uses the PSO-BP neural network to detect whether a DDoS attack occurs by further extracting the flow features of the abnormal *** show that compared with other methods,the detection accurate rate is guaranteed while the CPU load of the controller is reduced,and the detection capability is better.

关键词： software-defined networking Distributed Denial of Service attacks generalized information entropy Particle swarm optimization Back propagation neural network attack detection

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