为进一步提升应用层DDoS攻击检测准确率,提出一种将流量与用户行为特征相结合且模型参数可高效更新的应用层DDoS攻击检测模型.为统一处理流量与用户行为特征的异源数据,利用多模态深度(Multimodal Deep Learning,MDL)神经网络从数据流...为进一步提升应用层DDoS攻击检测准确率,提出一种将流量与用户行为特征相结合且模型参数可高效更新的应用层DDoS攻击检测模型.为统一处理流量与用户行为特征的异源数据,利用多模态深度(Multimodal Deep Learning,MDL)神经网络从数据流量与网页日志中提取流量与用户行为深层特征后输入汇聚深度神经网络进行检测.为减少MDL神经网络参数更新时的灾难性遗忘现象,在模型参数更新过程中基于弹性权重保持(Elastic Weight Consolidation,EWC)算法为重要模型参数增加惩罚项,保持对初始训练数据集检测准确率的同时,提升对新数据集的检测性能.最后,基于K-Means算法获得模型初始训练数据集聚类,并筛选出新数据集中聚类外数据进行模型参数更新,防止EWC算法因数据相关性过高而失效.实验表明,所提应用层DDoS检测模型检测准确率可达98.2%,且相对MLP_Whole方法模型参数更新性能较好.展开更多
Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty rem...Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.展开更多
文摘为进一步提升应用层DDoS攻击检测准确率,提出一种将流量与用户行为特征相结合且模型参数可高效更新的应用层DDoS攻击检测模型.为统一处理流量与用户行为特征的异源数据,利用多模态深度(Multimodal Deep Learning,MDL)神经网络从数据流量与网页日志中提取流量与用户行为深层特征后输入汇聚深度神经网络进行检测.为减少MDL神经网络参数更新时的灾难性遗忘现象,在模型参数更新过程中基于弹性权重保持(Elastic Weight Consolidation,EWC)算法为重要模型参数增加惩罚项,保持对初始训练数据集检测准确率的同时,提升对新数据集的检测性能.最后,基于K-Means算法获得模型初始训练数据集聚类,并筛选出新数据集中聚类外数据进行模型参数更新,防止EWC算法因数据相关性过高而失效.实验表明,所提应用层DDoS检测模型检测准确率可达98.2%,且相对MLP_Whole方法模型参数更新性能较好.
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
文摘Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.
