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
为了提高半导体器件小信号建模精度并解决优化算法易陷入局部最优解的问题,提出了一种基于改进斑马优化算法(Improved Zebra Optimization Algorithm,IZOA)的氮化镓高电子迁移率晶体管(Gallium Nitride High Electron Mobility Transist...为了提高半导体器件小信号建模精度并解决优化算法易陷入局部最优解的问题,提出了一种基于改进斑马优化算法(Improved Zebra Optimization Algorithm,IZOA)的氮化镓高电子迁移率晶体管(Gallium Nitride High Electron Mobility Transistor,GaN HEMT)混合小信号建模方法。采用数学修正法和直接提取法提取小信号参数,建立初步模型,再使用改进的斑马优化算法进一步提高建模的精度。对斑马优化算法(Zebra Optimization Algorithm,ZOA)的改进主要集中在三个方面:采用混沌映射提高初始种群多样性;使用反向学习策略扩大搜索范围;使用动态概率值替代固定值平衡搜索与收敛能力。实验结果表明,IZOA将直接提取法的平均误差从3.47%降至0.19%,相比灰狼优化(Grey Wolf Optimizer,GWO)算法(平均误差0.95%)降低0.76%,较标准ZOA(平均误差0.52%)降低0.33%,验证了算法的有效性和准确性。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.
文摘为了提高半导体器件小信号建模精度并解决优化算法易陷入局部最优解的问题,提出了一种基于改进斑马优化算法(Improved Zebra Optimization Algorithm,IZOA)的氮化镓高电子迁移率晶体管(Gallium Nitride High Electron Mobility Transistor,GaN HEMT)混合小信号建模方法。采用数学修正法和直接提取法提取小信号参数,建立初步模型,再使用改进的斑马优化算法进一步提高建模的精度。对斑马优化算法(Zebra Optimization Algorithm,ZOA)的改进主要集中在三个方面:采用混沌映射提高初始种群多样性;使用反向学习策略扩大搜索范围;使用动态概率值替代固定值平衡搜索与收敛能力。实验结果表明,IZOA将直接提取法的平均误差从3.47%降至0.19%,相比灰狼优化(Grey Wolf Optimizer,GWO)算法(平均误差0.95%)降低0.76%,较标准ZOA(平均误差0.52%)降低0.33%,验证了算法的有效性和准确性。
