一种基于OpenFlow的SDN访问控制策略实时冲突检测与解决方法

软件定义网络SDN(Software-Defined Networking)是由美国斯坦福大学Clean Slate研究组提出的一种新型网络创新架构,可通过软件编程的形式定义和控制网络,其控制平面和转发平面分离及开放性可编程的特点,为新型互联网体系结构研究提供了新的实验途径,也极大地推动了下一代互联网的发展.OpenFlow是SDN的主要协议,定义了SDN控制器与交换机之间的通信标准.目前,很多基于OpenFlow的SDN设备已经在实际中得到了部署.但是,基于OpenFlow的SDN却面临很多安全挑战.其中一个重要的挑战是如何建立一个安全可靠的SDN防火墙应用.由于OpenFlow协议的无状态性,现有的SDN防火墙可以被通过改写交换机中的流表项轻松绕过.针对这一安全威胁,作者提出了基于Flowpath的实时动态策略冲突检测与解决方法.通过获取实时的SDN网络状态,能够准确地检测防火墙策略的直接和间接违反,并且一旦发现冲突,可以基于Flowpath进行自动化和细粒度的冲突解决.最后,作者在开源控制器Floodlight上实现了一个安全增强的防火墙应用FlowVerifier,并基于Mininet对FlowVerifier的性能进行了评估.结果表明FlowVerifier能够检测和自动化地解决SDN网络中由于流表改写而引入的策略冲突及其带来的安全威胁.     

非标准约束恒模盲多用户检测的改进算法研究

针对码分多址蜂窝移动通信中存在的多址干扰问题,以瑞利分布为变步长,构造了基于瑞利分布变步长的非标准约束恒模算法;与差分形式相结合,提出了基于瑞利分布变步长的非标准约束差分恒模算法(RDV-NSCDCMA);分析了此算法的性能并与非标准约束恒模算法(NSCCMA)进行了仿真比较.     

基于ZigBee的矿用应力监测系统的研究

提出了一种新的煤矿巷道监测方法 ,即采用测力锚杆对巷道顶部深处的应力变化进行定时监测。系统主要分为三部分:传感器节点、通信主站和上位机。传感器节点负责采集和上传数据,数据主站负责管理各传感器节点并存储和上传数据,上位机负责汇总各传感器数据并对其进行分析处理。     

A two‐level detector of short‐term unique changes in time series based on a similarity method

In the paper, a novel two‐level algorithm of time‐series change detection is presented. In the first level, to identify non‐stationary sequences in a processed signal, preliminary detection of events is performed with a short‐term prediction comparison. In the second stage, to confirm the changes detected in the first level, a similarity method aimed at identification of unique changes is employed. The detection of changes in a non‐stationary time series is discussed, implemented algorithms are described and the results produced on a sample four financial time series are shown. General conditions for implementing the proposed algorithm as an immune‐like event detector are discussed.     

基于Hadoop的云端异常流量检测与分析平台

Hadoop系统作为一种开源的分布式云计算平台已获得广泛应用,但其云端易受到各种威胁和攻击,基于此,开发了一种基于Hadoop的云端异常流量检查与分析平台。首先,使用Mapper周期性地从所有存储流量信息的文件中提取流量的部分信息;然后,通过Reducer将异常流量提取并保存。通过对流量数据的存储、检测与分析可成功地检测出有威胁的攻击,从而保障云端的安全。由于本平台基于开源的Hadoop实现,因此成本较低;同时,基于Java语言实现,可成功移植于各种主流操作系统,具有广泛适用性。基于局域网进行监控试验,结果表明本平台可成功地检测出异常流量,并输出友好的用户界面。     

Improving classification-based diagnosis of batch processes through data selection and appropriate pretreatment

This work considers the application of classification algorithms for data-driven fault diagnosis of batch processes. A novel data selection methodology is proposed which enables online classification of detected disturbances without requiring the estimation of unknown (future) process behavior, as is the case in previously reported approaches.The proposed method is benchmarked in two case studies using the Pensim process model of Birol et al. (2002) implemented in RAYMOND. Both a simple k Nearest Neighbors (k-NN) and complex Least Squares Support Vector Machine (LS-SVM) are employed for classification to demonstrate the generic nature of the proposed approach. In addition, the influence of different data pretreatment methods on the classification performance is discussed, together with a motivation for selecting the correct pretreatment steps. Finally, the influence of the number of available training batches is studied.The results demonstrate that a good classification performance can be achieved with the proposed data selection method even with a low number of faulty training batches by exploiting knowledge on the nature of the to-be-diagnosed faults in the data pretreatment. This provides a proof of concept for classification-based batch diagnosis and demonstrates the importance of incorporating process insight in the construction of data-driven process monitoring and diagnosis tools.     

Mooring system diagnosis and structural reliability control for position moored vessels

Early diagnosis and fault-tolerant control are essential for safe operation of floating platforms where mooring systems maintain vessel position and must withstand environmental loads. This paper considers two critical faults, line breakage and loss of a buoyancy element and employs vector statistical change detection for timely diagnosis of faults. Diagnosis design is scrutinised and a procedure is proposed based on specified false alarm probability and estimation of the distribution of the test statistics on which change detection is based. A structural reliability index is applied for monitoring the safety level of each mooring line and a set-point chasing algorithm accommodates the effects of line failure, as an integral part of the reliability-based set-point chasing control algorithm. The feasibility of the diagnosis and of the fault-tolerant control strategy is verified in model basin tests.     

面向大规模微博消息流的突发话题检测

突发事件在微博中迅速传播,产生巨大的影响力,因此,突发舆情受到政府、企业的广泛关注.现有的突发话题检测算法只考虑单一的特征实体,无法处理微博中新词、图片、链接等诱导的突发.面向大规模微博消息流,提出一种无需中文分词的实时突发话题检测框架模型.模型依据消息流动态调整窗口大小,并通过传播影响力度量实体的突发权值.采用高阶联合聚类算法同时对实体、消息、用户进行聚类分析,在检测突发话题的同时,得到话题的关联消息及参与用户.对比实验结果表明,算法的准确性高,能够更早地检测到突发话题.     

Detection of delamination in laminated composites with limited measurements combining PCA and dynamic QPSO

This paper presents an output only damage diagnostic algorithm based on frequency response functions and the principal components for health monitoring of laminated composite structures. The principal components evaluated from frequency response data, are employed as dynamical invariants to handle the effects of operational/environmental variability on the dynamic response of the structure. Finite element models of a laminated composite beam and plate are used to generate vibration data for healthy and damaged structures. Three numerical examples include a laminated composite beam, cantilever plate made of carbon–epoxy and a laminated composite simply supported plate. Varied levels of delamination of laminated composite plies and matrix cracking at varied locations in the plies are simulated at different spatial locations of the structure. Numerical investigations have been carried out to identify the spatial location of damage using the proposed principal component analysis (PCA) based algorithm. In order to limit the number of sensors on the structure, an optimal sensor placement algorithm based on PCA is employed in the present work and the effectiveness of the proposed algorithm with a limited number of sensors is also investigated. Finally, the inverse problem associated with the detection of delamination and matrix cracking is formulated as an optimization problem and is solved using the newly developed dynamic quantum particle swarm optimization (DQPSO) algorithm. Studies carried out and presented in this paper clearly indicate that the proposed SHM scheme can robustly identify the instant of damage, spatial location, the extent of delamination and matrix cracking even with limited sensor measurements and also with noisy data.     

高速铁路沿线土地利用/覆盖变化检测方法研究

为开展高速铁路沿线土地利用/覆盖的快速变化检测工作,本文首先对两期高分辨率遥感影像进行主成分分析得到两期影像的第一主成分量,再用GIS矢量数据分割两期第一主成分量得到影像对象层,构造对象的灰度均值向量特征,最后利用最大类间方差阈值分割算法(Otsu法),自动提取变化类与未变化类。为验证该方法在高速铁路沿线土地利用/覆盖变化检测应用中的有效性,本文对2009年与2012年武广高铁武江特大桥沿线的GeoEye影像(分辨率1.6m)进行了变化检测验证性实验,得到变化检测结果的精度能够达到84.7579%,仅耗时5s。在此基础上将该方法应用于2009年与2010年石武客专郑州东站段WorldView-2影像(分辨率2.0m)的变化检测中,得到的变化检测结果精度为88.6953%,耗时86s。可见,该方法在检测高速铁路沿线土地利用/覆盖变化中具有较高的可靠性和效率,对更新高速铁路数据库具有一定的使用价值。