The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion dete...The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks.For attack detection,the prior system has created an SMSRPF(Stacking Model Significant Rule Power Factor)classifier.To provide creative instance detection,the SMSRPF combines the detection of trained classifiers such as DT(Decision Tree)and RF(Random Forest).Nevertheless,it does not generate any accuratefindings that are adequate.The suggested system has built an EWF(Ensemble Wrapper Filter)feature selection with SMSRPF classifier for attack detection so as to overcome this problem.The UNSW-NB15 dataset is used as an input in this proposed research project.Specifically,min–max normalization approach is used to pre-process the incoming data.The feature selection is then carried out using EWF.Based on the selected features,SMSRPF classifiers are utilized to detect the attacks.The SMSRPF is integrated with the trained classi-fiers such as DT and RF to create creative instance detection.After that,the testing data is classified using MCAR(Multi-Class Classification based on Association Rules).The SRPF judges the rules correctly even when the confidence and the lift measures fail.Regarding accuracy,precision,recall,f-measure,computation time,and error,the experimental findings suggest that the new system outperforms the prior systems.展开更多
In the previous paper, in order to express steadystate ratchetting, the present s extended the cyclic plasticity model proposed by Ohno and Wang (1993), and the validity of the extended model was discussed on the ba...In the previous paper, in order to express steadystate ratchetting, the present s extended the cyclic plasticity model proposed by Ohno and Wang (1993), and the validity of the extended model was discussed on the basis of uniaxial ratchetting experiments of 316FR steel at room temperature. In the present paper, the validity of the extended model is discussed further on the basis of nonproportional experiments of IN738LC at 850 such as multiaxial ratchetting, multiaxial cyclic stress relaxation, circular cyclic straining with strain hold, and so on. Predictions based on the OhnoWang model as well as the ArmstrongFrederick model are also given for the sake of comparison. It is shown that the extended model is capable of simulating the nonproportional experiments accurately, and especially that the extended model can predict much less steadystate ratchetting than the ArmstrongFrederick model. It is also shown that the extended model provides almost the same predictions as the OhnoWang and th展开更多
文摘The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks.For attack detection,the prior system has created an SMSRPF(Stacking Model Significant Rule Power Factor)classifier.To provide creative instance detection,the SMSRPF combines the detection of trained classifiers such as DT(Decision Tree)and RF(Random Forest).Nevertheless,it does not generate any accuratefindings that are adequate.The suggested system has built an EWF(Ensemble Wrapper Filter)feature selection with SMSRPF classifier for attack detection so as to overcome this problem.The UNSW-NB15 dataset is used as an input in this proposed research project.Specifically,min–max normalization approach is used to pre-process the incoming data.The feature selection is then carried out using EWF.Based on the selected features,SMSRPF classifiers are utilized to detect the attacks.The SMSRPF is integrated with the trained classi-fiers such as DT and RF to create creative instance detection.After that,the testing data is classified using MCAR(Multi-Class Classification based on Association Rules).The SRPF judges the rules correctly even when the confidence and the lift measures fail.Regarding accuracy,precision,recall,f-measure,computation time,and error,the experimental findings suggest that the new system outperforms the prior systems.
文摘In the previous paper, in order to express steadystate ratchetting, the present s extended the cyclic plasticity model proposed by Ohno and Wang (1993), and the validity of the extended model was discussed on the basis of uniaxial ratchetting experiments of 316FR steel at room temperature. In the present paper, the validity of the extended model is discussed further on the basis of nonproportional experiments of IN738LC at 850 such as multiaxial ratchetting, multiaxial cyclic stress relaxation, circular cyclic straining with strain hold, and so on. Predictions based on the OhnoWang model as well as the ArmstrongFrederick model are also given for the sake of comparison. It is shown that the extended model is capable of simulating the nonproportional experiments accurately, and especially that the extended model can predict much less steadystate ratchetting than the ArmstrongFrederick model. It is also shown that the extended model provides almost the same predictions as the OhnoWang and th
