In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extract...In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extraction ability,and they cannot mine the discriminating features of the protocol data thoroughly.To address the issue,we propose an unknown application layer protocol recognition method based on deep clustering.Deep clustering which consists of the deep neural network and the clustering algorithm can automatically extract the features of the input and cluster the data based on the extracted features.Compared with the traditional clustering methods,deep clustering boasts of higher clustering accuracy.The proposed method utilizes network-in-network(NIN),channel attention,spatial attention and Bidirectional Long Short-term memory(BLSTM)to construct an autoencoder to extract the spatial-temporal features of the protocol data,and utilizes the unsupervised clustering algorithm to recognize the unknown protocols based on the features.The method firstly extracts the application layer protocol data from the network traffic and transforms the data into one-dimensional matrix.Secondly,the autoencoder is pretrained,and the protocol data is compressed into low dimensional latent space by the autoencoder and the initial clustering is performed with K-Means.Finally,the clustering loss is calculated and the classification model is optimized according to the clustering loss.The classification results can be obtained when the classification model is optimal.Compared with the existing unknown protocol recognition methods,the proposed method utilizes deep clustering to cluster the unknown protocols,and it can mine the key features of the protocol data and recognize the unknown protocols accurately.Experimental results show that the proposed method can effectively recognize the unknown protocols,and its performance is better than other methods.展开更多
基于智能电网融合大数据分析技术,探索海量数据的潜在价值,以实现反窃查违。文中采用高速电力线载波(High-speed Power Line Carrier,HPLC)数据传输技术,对采集到的智能电表的96个时刻电参量进行数据处理,通过引入户表间电压降提出表间...基于智能电网融合大数据分析技术,探索海量数据的潜在价值,以实现反窃查违。文中采用高速电力线载波(High-speed Power Line Carrier,HPLC)数据传输技术,对采集到的智能电表的96个时刻电参量进行数据处理,通过引入户表间电压降提出表间关系距离矩阵、户表标签和户表相似度的概念,以户表相似度计算传输阻抗,确定台区合理线损。结合实际情况下的户表聚类处理和线损识别,验证了构建基于聚类分析的台区传输阻抗特性及线损模型的可靠性,能够精准判别每个台区线损是否正常,正确率达到99%,为反窃查违提供了重要线索。展开更多
基于39GHz室外微蜂窝场景实测数据,开展了毫米波段路径损耗、阴影衰落和大尺度参数的建模与仿真研究.介绍了毫米波段喇叭旋转测量系统下空间交替广义期望最大化(Space-Alternating Generalized Expectation-maximization,SAGE)算法信号...基于39GHz室外微蜂窝场景实测数据,开展了毫米波段路径损耗、阴影衰落和大尺度参数的建模与仿真研究.介绍了毫米波段喇叭旋转测量系统下空间交替广义期望最大化(Space-Alternating Generalized Expectation-maximization,SAGE)算法信号模型,优化的分簇算法与莱斯因子计算方法.基于SAGE提取多径参数,利用优化的分簇算法提取并分析了簇参数,包括簇内角度扩展、簇内时延扩展以及簇的数目,并根据测量结果验证了第三代合作伙伴计划(The 3rd Generation Partnership Project,3GPP)第五代(the 5th Generation,5G)移动通信标准推荐的仿真平台准确定性无线信道产生器(Quasi-Deterministic Radio Channel Generator,QuaDRiGa)在39GHz的可用性.结果表明:在视距径下,方向性路损和全向路损在固定截距和浮动截距两种拟合方式下与自由空间路损模型接近;大尺度参数统计特性与基于毫米波的第五代集成通信移动无线电接入网络(Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications,mmMAGIC)、3GPP结论接近;视距径与非视距径的簇参数差别较小,且簇的个数较6GHz下的频段更少.本文为5G毫米波39GHz频段信道仿真和系统设计提供了重要的信道模型和参数.展开更多
基金This work is supported by the National Key R&D Program of China(2017YFB0802900).
文摘In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extraction ability,and they cannot mine the discriminating features of the protocol data thoroughly.To address the issue,we propose an unknown application layer protocol recognition method based on deep clustering.Deep clustering which consists of the deep neural network and the clustering algorithm can automatically extract the features of the input and cluster the data based on the extracted features.Compared with the traditional clustering methods,deep clustering boasts of higher clustering accuracy.The proposed method utilizes network-in-network(NIN),channel attention,spatial attention and Bidirectional Long Short-term memory(BLSTM)to construct an autoencoder to extract the spatial-temporal features of the protocol data,and utilizes the unsupervised clustering algorithm to recognize the unknown protocols based on the features.The method firstly extracts the application layer protocol data from the network traffic and transforms the data into one-dimensional matrix.Secondly,the autoencoder is pretrained,and the protocol data is compressed into low dimensional latent space by the autoencoder and the initial clustering is performed with K-Means.Finally,the clustering loss is calculated and the classification model is optimized according to the clustering loss.The classification results can be obtained when the classification model is optimal.Compared with the existing unknown protocol recognition methods,the proposed method utilizes deep clustering to cluster the unknown protocols,and it can mine the key features of the protocol data and recognize the unknown protocols accurately.Experimental results show that the proposed method can effectively recognize the unknown protocols,and its performance is better than other methods.
文摘基于智能电网融合大数据分析技术,探索海量数据的潜在价值,以实现反窃查违。文中采用高速电力线载波(High-speed Power Line Carrier,HPLC)数据传输技术,对采集到的智能电表的96个时刻电参量进行数据处理,通过引入户表间电压降提出表间关系距离矩阵、户表标签和户表相似度的概念,以户表相似度计算传输阻抗,确定台区合理线损。结合实际情况下的户表聚类处理和线损识别,验证了构建基于聚类分析的台区传输阻抗特性及线损模型的可靠性,能够精准判别每个台区线损是否正常,正确率达到99%,为反窃查违提供了重要线索。
文摘基于39GHz室外微蜂窝场景实测数据,开展了毫米波段路径损耗、阴影衰落和大尺度参数的建模与仿真研究.介绍了毫米波段喇叭旋转测量系统下空间交替广义期望最大化(Space-Alternating Generalized Expectation-maximization,SAGE)算法信号模型,优化的分簇算法与莱斯因子计算方法.基于SAGE提取多径参数,利用优化的分簇算法提取并分析了簇参数,包括簇内角度扩展、簇内时延扩展以及簇的数目,并根据测量结果验证了第三代合作伙伴计划(The 3rd Generation Partnership Project,3GPP)第五代(the 5th Generation,5G)移动通信标准推荐的仿真平台准确定性无线信道产生器(Quasi-Deterministic Radio Channel Generator,QuaDRiGa)在39GHz的可用性.结果表明:在视距径下,方向性路损和全向路损在固定截距和浮动截距两种拟合方式下与自由空间路损模型接近;大尺度参数统计特性与基于毫米波的第五代集成通信移动无线电接入网络(Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications,mmMAGIC)、3GPP结论接近;视距径与非视距径的簇参数差别较小,且簇的个数较6GHz下的频段更少.本文为5G毫米波39GHz频段信道仿真和系统设计提供了重要的信道模型和参数.
