Having analyzed the relationships between washing shrinkage and weaving technique, parameters, material properties of woven fabrics and studied the shrinkage mechanism and its mathematical model of the plain fabric, r...Having analyzed the relationships between washing shrinkage and weaving technique, parameters, material properties of woven fabrics and studied the shrinkage mechanism and its mathematical model of the plain fabric, researchers set up a shrinkage model of the twills and satins and proposed a method for calculating the washing shrinkage based on weaving technique and parameters of fabrics. Shrinkage experiments of silk habotai, silk twill and silk satin fabrics were performed. The results were compared with those of the theoretical computations, and theoretical method is reliable.展开更多
Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control ce...Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network programming.However,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in SDN.To address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is proposed.This approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection module.The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on DNN.DDoS assaults were found when suspected irregular traffic was validated.Experiments reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy rate.The false alarm rate(FAR)is much lower than that of the information entropy-based detection method.Simultaneously,the proposed framework can shorten the detection time and improve the resource utilization efficiency.展开更多
基金Qinglan Project of Jiangsu Province,China(No.SJ2007-02)
文摘Having analyzed the relationships between washing shrinkage and weaving technique, parameters, material properties of woven fabrics and studied the shrinkage mechanism and its mathematical model of the plain fabric, researchers set up a shrinkage model of the twills and satins and proposed a method for calculating the washing shrinkage based on weaving technique and parameters of fabrics. Shrinkage experiments of silk habotai, silk twill and silk satin fabrics were performed. The results were compared with those of the theoretical computations, and theoretical method is reliable.
基金This publication was supported by the Ministry of Education,Malaysia(Grant code:FRGS/1/2018/ICT02/UKM/02/6).
文摘Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network programming.However,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in SDN.To address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is proposed.This approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection module.The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on DNN.DDoS assaults were found when suspected irregular traffic was validated.Experiments reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy rate.The false alarm rate(FAR)is much lower than that of the information entropy-based detection method.Simultaneously,the proposed framework can shorten the detection time and improve the resource utilization efficiency.
