Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol...Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.展开更多
In permanent magnet synchronous motor(PMSM)control,the jitter problem affects the system performance,so a novel reaching lawis proposed to construct a non-singular fast terminal slidingmode controller(NFTSMC)to reduce...In permanent magnet synchronous motor(PMSM)control,the jitter problem affects the system performance,so a novel reaching lawis proposed to construct a non-singular fast terminal slidingmode controller(NFTSMC)to reduce the jitter.To enhance the immunity of the system,a disturbance observer is designed to observe and compensate for the disturbance to the sliding mode controller.In addition,considering that the controller parameters are difficult to adjust,and the traditional zebra optimization algorithm(ZOA)is prone to converge prematurely and fall into local optimum when solving the optimal solution,the improved zebra optimization algorithm(IZOA)is proposed,and the ability of the IZOA in practical applications is verified by using international standard test functions.To verify the performance of IZOA,firstly,the adjustment time of IZOA is reduced by 71.67%compared with ZOA through the step response,and secondly,the tracking error of IZOA is reduced by 51.52%compared with ZOA through the sinusoidal signal following.To verify the performance of the designed controller based on disturbance observer,the designed controller reduces the speed overshoot from 2.5%to 0.63%compared with the traditional NFTSMC in the speed mutation experiment,which is a performance improvement of 70.8%,and the designed controller outperforms the traditional NFTSMC in the load mutation experiment,which is a performance improvement of 60.0%in the case of sudden load addition,and a performance improvement of 90.0%in the case of load release,which verifies that the designed controller outperforms the traditional NFTSMC.展开更多
基金supported by Science and Technology Innovation Programfor Postgraduate Students in IDP Subsidized by Fundamental Research Funds for the Central Universities(Project No.ZY20240335)support of the Research Project of the Key Technology of Malicious Code Detection Based on Data Mining in APT Attack(Project No.2022IT173)the Research Project of the Big Data Sensitive Information Supervision Technology Based on Convolutional Neural Network(Project No.2022011033).
文摘Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.
基金supported by the Key Technology of Flexible Regulation of Energy in Green High-Efficiency/Carbon-Efficient Buildings under the Smart Park System of PowerChina Guiyang Co.,Ltd.(YJ2022-12)the Science and Technology Support Plan of Guizhou Province“Research and Application Development of Key Technologies for Flexible Regulation of Energy in High-Efficiency/Carbon-Efficient Buildings”(Guizhou Science and Technology Cooperation Support[2023]General 409).
文摘In permanent magnet synchronous motor(PMSM)control,the jitter problem affects the system performance,so a novel reaching lawis proposed to construct a non-singular fast terminal slidingmode controller(NFTSMC)to reduce the jitter.To enhance the immunity of the system,a disturbance observer is designed to observe and compensate for the disturbance to the sliding mode controller.In addition,considering that the controller parameters are difficult to adjust,and the traditional zebra optimization algorithm(ZOA)is prone to converge prematurely and fall into local optimum when solving the optimal solution,the improved zebra optimization algorithm(IZOA)is proposed,and the ability of the IZOA in practical applications is verified by using international standard test functions.To verify the performance of IZOA,firstly,the adjustment time of IZOA is reduced by 71.67%compared with ZOA through the step response,and secondly,the tracking error of IZOA is reduced by 51.52%compared with ZOA through the sinusoidal signal following.To verify the performance of the designed controller based on disturbance observer,the designed controller reduces the speed overshoot from 2.5%to 0.63%compared with the traditional NFTSMC in the speed mutation experiment,which is a performance improvement of 70.8%,and the designed controller outperforms the traditional NFTSMC in the load mutation experiment,which is a performance improvement of 60.0%in the case of sudden load addition,and a performance improvement of 90.0%in the case of load release,which verifies that the designed controller outperforms the traditional NFTSMC.
