基于柔性神经树的Internet流量早期识别模型

在互联网产生的早期阶段对其进行准确有效的识别,对于网络管理和网络安全来说都有着极其重要的意义。鉴于此,近年来越来越多的研究致力于仅仅基于流量早期的数个数据包,建立有效的机器学习模型对其进行识别。本文力图基于柔性神经树(FNT)构建有效的互联网流量早期识别模型。两个开放数据集和一个实验室采集的数据集用于实验研究,并将FNT与8种经典算法进行对比。实验结果表明,FNT在大多数情况下,其识别率和误报率指标优于其他算法,这说明FNT是一种有效的流量早期识别模型。     

面向轻载深包检测的应用负载特征自动挖掘(英文)

Automatic signature generation approaches have been widely applied in recent traffic classification.However,they are not suitable for LightWeight Deep Packet Inspection(LW_DPI) since their generated signatures are matched through a search of the entire application data.On the basis of LW_DPI schemes,we present two Hierarchical Clustering(HC) algorithms:HC_TCP and HC_UDP,which can generate byte signatures from TCP and UDP packet payloads respectively.In particular,HC_TCP and HC_ UDP can extract the positions of byte signatures in packet payloads.Further,in order to deal with the case in which byte signatures cannot be derived,we develop an algorithm for generating bit signatures.Compared with the LASER algorithm and Suffix Tree(ST)-based algorithm,the proposed algorithms are better in terms of both classification accuracy and speed.Moreover,the experimental results indicate that,as long as the application-protocol header exists,it is possible to automatically derive reliable and accurate signatures combined with their positions in packet payloads.     

早期流量识别中多少个数据包最有效的实验研究(英文)

Accurately identifying network traffics at the early stage is very important for the application of traffic identification.Recent years,more and more research works have tried to build effective machine learning models to identify traffics with the few packets at the early stage.However,a basic and important problem is still unresolved,that is how many packets are most effective in early stage traffic identification.In this paper,we try to resolve this problem using experimental methods.We firstly extract the packet size of the first 2-10 packets of 3 traffic data sets.And then execute crossover identification experiments with different numbers of packets using 11 well-known machine learning classifiers.Finally,statistical tests are applied to find out which number is the best performed one.Our experimental results show that 5-7are the best packet numbers for early stage traffic identification.     

纯Fe触媒体系下C3N6H6对宝石级金刚石生长的影响

采用温度梯法在纯铁触媒体系下,通过掺入三聚氰胺(C3N6H6)进行宝石级金刚石的合成,探究氮氢对合成宝石级金刚石的形貌、颜色影响.实验结果表明在纯铁触媒的体系中,随着三聚氰胺添加量的增加,宝石的颜色由棕黄色变为浅绿色;红外光谱检测显示,随着三聚氰胺添加量的增加,样品中氮得得含量逐渐增多,并且伴随着宝石级金刚石从样貌形态及内在物质含量上发生了明显变化.     

XML安全技术在水泥网格中的应用

网格计算是基于网格结构的问题求解模式,它将网络上分布的计算机资源组织起来协同解决复杂的科学与工程计算问题。我们基于Globus项目的研究成果及其工具包,开发了面向水泥材料设计与仿真的网格系统。本文结合XML安全技术及水泥网格自身的特点。研究了网格结点间的信任体系和安全策略。提出了一种用将XML签名及XML加密技术应用于网格计算的方法。     

TL:采集具有准确应用用信息的Internet流量样本（英文）

Traffic classification research has been suffering from a trouble of collecting accurate samples with ground truth.A model named Traffic Labeller(TL) is proposed to solve this problem.TL system captures all user socket calls and their corresponding application process information in the user mode on a Windows host.Once a sending data call has been captured,its 5-tuple {source IP,destination IP,source port,destination port and transport layer protocol},associated with its application information,is sent to an intermediate NDIS driver in the kernel mode.Then the intermediate driver writes application type information on TOS field of the IP packets which match the 5-tuple.In this way,each IP packet sent from the Windows host carries their application information.Therefore,traffic samples collected on the network have been labelled with the accurate application information and can be used for training effective traffic classification models.     

基于形状改变能密度的岩石强度准则研究

分析砂岩在常规三轴压缩试验过程中弹性应变能密度、体积改变能密度和形状改变能密度的演化规律,根据峰值形状改变能密度与围压的线性变化关系建立形状改变能密度岩石强度准则。结果表明：（1）体积改变能密度与形状改变能密度,在峰前阶段逐渐增加,峰后急剧下降;（2）形状改变能密度强度准则参数物理意义明确,且可通过参数变换转化为经典的Hoek-Brown准则;（3）形状改变能密度强度准则计算结果与8种不同类型岩石的常规三轴压缩试验结果基本一致,验证了准则的精确性和适用性。