Towards a Secure IPv6 Autoconfiguration

ABSTRACT

Stateless Address Autoconfiguration (SLAAC) is one of the novel features introduced in IPv6 Neighbor Discovery Protocol (NDP) providing for self-configuration of nodes and supplementing the reduction of operational and deployment cost of networks. Although self-configuration elevates the idea of autonomy for network, it has also introduced vulnerabilities that require substantial solutions. The SLAAC is pivoted on the presumption that network consists of authentic and entrusted nodes, however with inception of public sector wireless networks; any node can affix to the link with trivial authentication and situation changes radically. Although some security extensions like IPsec or SeND have been proposed, but these security protocols have been reported to have serious limitations like complex cryptographic algorithms which negate their adoption. This paper revisits the stateless auto configuration process and discusses its inherent vulnerabilities. The paper surveys existing research and available defense mechanisms available to protect SLAAC. The paper also suggests some guidelines from existing literature which can further promote and supplement the research to secure the auto configuration process. Finally, a novel technique is proposed for securing IPv6 link layer communication against DoS and Man-in-the-Middle attack which can be used as an alternate approach for CGA and SeND protocol.     

基于不重叠路径熵的网络抗毁性评估方法

通过引入熵的概念,提出了基于不重叠路径数的标准稳定熵指标,用于刻画节点间抗毁性随不重叠路径数变化的规律.在此基础上给出了用于全网抗毁性评估的模型.同时指出了评价模型中涉及的节点间不重叠路径数的计算方法.最后通过算例分析以及与最短路径法相比较,论证了基于不重叠路径熵的模型在网络抗毁性评估方面具有更高的准确性与合理性.     

Feature correspondence in a non-overlapping camera network

Person re-identification across multiple cameras is difficult due to viewpoint and illumination variations. Most traditional research focuses on developing invariant features that are unaffected by these variations. However, thus far, there has been no feature developed that is completely invariant, and it is possible that a fully invariant feature may not exist. Therefore, we do not seek to develop these ideal features in this paper. We instead propose a framework for learning a gallery of persons who appear in the camera network frequently. The gallery contains appearance models of these persons from each camera and viewpoint. Given the camera identity, viewpoint identity, person identity, the model is decided. Since these appearance models are specific to each camera and viewpoint, the problems of viewpoint variations and illumination variations between cameras are explicitly solved, and re-identification becomes a ranking problem. Experiments demonstrate that our framework provides significant improvement in addressing the re-identification problem.     

Automatic resource allocation in a distributed camera network

In this paper, we present a hierarchical smart resource coordination and reconfiguration framework for distributed systems. We view the coordination problem as one of context aware resource reconfiguration. The fundamental unit in this hierarchy is a Fault Containment Unit (FCU) that provides run-time fault-tolerance by deciding on the best alternative course of action when a failure occurs. FCUs are composed hierarchically and are responsible for dynamically reconfiguring failing FCUs at lower levels. When such a reconfiguration is not possible, FCUs propagate the failure upward for resolution. We evaluate the effectiveness of our framework in a people tracking application using a network of cameras. The task for our multi-camera network is to allocate pairs of cameras that localize a subject optimally given the current run-time context. The system automatically derives policies for switching between camera pairs that enable robust tracking while being attentive to certain performance measures. Our approach is unique in that we model the dynamics in the scene and the camera network configuration steers the policies to provide robust tracking.     

Prowling autonomous mobile robot with a network camera

This research aimed to develop an autonomous mobile robot that helps various kinds of people. The evasion of obstacles is absolutely imperative so that the robot can act in a human-life environment. Therefore, we developed a robot that moves through doors and avoids obstacles with the help of images taken by a camera set on the robot. This work was presented in part at the 13th International Symposium on Artifical Life and Robotics, Oita, Japan, January 31–February 2, 2008     

网络摄像机的设计

设计了数字化视频监控系统.系统采用C/S架构设计,由网络摄像机、监控客户端以及网络设备组成.选择具有硬件MPEG-4压缩功能的处理器FIC8120设计了网络摄像机.详细介绍了网络摄像机的软硬件设计过程及组成部分.网络设备选择校园局域网络,同时也将其作为了测试的媒介.该系统经过功能扩展后可以应用到很多领域,如远程会议、视...     

直线运动摄像机在线动态标定

为实现AS-R智能机器人在运动情况下摄像机在线动态标定,提出一种新的基于粒子滤波的直线运动摄像机标定方法。用状态空间方法描述直线运动摄像机模型,把摄像机内参数和位置运动参数作为状态量,特征点图像坐标作为观测量,根据粒子滤波算法求得摄像机内参数和位置运动参数的最优估计,并用双线程实现整个标定过程。AS-R机器人在直线运动情况下的摄像机在线动态标定实验结果表明：该算法是合理可行的,并且具有很高的标定精度和良好的鲁棒性。该方法适用于各种类型的系统噪声。     

摄像机神经网络标定技术

利用人工神经网络来直接学习图像信息与三维信息之间的关系，不需确定摄像机具体的内部参数和外部参数，也不需知道有关模型或参数的先先验知识。在双目视觉的情况下，两摄像机的位置关系不需具体求出，而是隐含在神经网络之中。实验结果表明神经网络方法的标定结果令人满意，并成功地用于机器人曲线跟踪的实验。     

新一代协议IPv6自动地址配置的实现

本文分析了 IPv6协议的特点、协议过度方案及在新协议实施时自动地址配置的必要性 .探讨了两种自动地址配置方式的独立性和互补性 ,从系统和工程角度阐述了 DHCPv6系统的实现过程 ,并在 Free BSD4.0系统下实现了系统的三个部分 ,最后讨论了基于 Free BSD系统的改进问题     

TDI-CCD相机动态成像的速度同步控制研究

在介绍时间延迟积分电荷耦合器件(TD I-CCD)的结构与工作原理基础上,通过像移调制传递函数(MTF)分析了TD I-CCD行扫速率与运动像不同步时对相机成像质量的影响。理论和实验仿真表明:在奈奎斯特频率范围内,只要将TD I-CCD推扫成像过程中的同步误差控制在一定的范围内,就可以明显地减少像移对成像质量的影响,最后,给出了2种简便和实用的同步控制方法。     

Intelligent unmanned anti-theft system using network camera

In this paper, we propose the intelligent unmanned anti-theft system using network cameras. To do this, we use two Internet Protocol (IP) cameras, which are installed at the inside and the outside of the restricted area. First of all, the external camera is used to monitor a potential intruder entering the restricted area in real time. To detect the potential intruder from sequential images obtained by external IP camera, we propose the robust algorithm combining the background modeling, the hybrid silhouette, the convex summation, the morphology and the smoothing. Once the intruder is detected by the external camera, the internal camera with pan-tilt mechanism is then activated and starts tracking the intruder and monitoring the prescribed valuables registered with some feature points in the database. To track the intruder, we propose the tracking algorithm using the background elimination and the histogram. And then, we completes the proposed anti-theft system by using the Scale Invariant Feature Transform (SIFT) algorithm in order to monitor whether the intruder steals the prescribed valuables or not. Finally, we have shown the effectiveness and the applicability of the proposed method through experiments.     

基于自适应神经网络的双摄像机标定

摄像机标定是计算机视觉中非常重要的一环,传统标定方法需要求解内外参数,非常繁琐。通过建立自适应神经网络直接学习图像坐标与空间坐标间的关系。该方法对Harris角点提取结果进行增加约束的改进,从而提高网络训练样本精度,通过程序实现隐层神经元的自适应选取,并综合运用正则化、提前终止策略,使网络的泛化能力得到极大的改善。最后通过与经典标定方法进行对比的实验证明基于自适应神经网络具有很好的双摄像机标定精度。     

Auto-organized visual perception using distributed camera network

Camera networks are complex vision systems difficult to control if the number of sensors is getting higher. With classic approaches, each camera has to be calibrated and synchronized individually. These tasks are often troublesome because of spatial constraints, and mostly due to the amount of information that need to be processed. Cameras generally observe overlapping areas, leading to redundant information that are then acquired, transmitted, stored and then processed. We propose in this paper a method to segment, cluster and codify images acquired by cameras of a network. The images are decomposed sequentially into layers where redundant information are discarded. Without the need of any calibration operation, each sensor contributes to build a global representation of the entire network environment. The information sent by the network is then represented by a reduced and compact amount of data using a codification process. This framework allows structures to be retrieved and also the topology of the network. It can also provide the localization and trajectories of mobile objects. Experiments will present practical results in the case of a network containing 20 cameras observing a common scene.     

p-period maximal dynamic flows in a network

Given a network with a source and a destination vertices, fixed traversal times, time-dependent capacities of edges, and permissible held-over periods at the vertices. The p-period maximal dynamic flow problem is to find the maximal flow that can be transferred from the source to the destination within p periods of time. The paper presents an ulgorithm for the solution of this problem, reviews previous results for simpler cases and reports on some limited computational experience.     

Simultaneous planning,localization, and mapping in a camera sensor network

In this paper we examine issues of localization, exploration, and planning in the context of a hybrid robot/camera-network system. We exploit the ubiquity of camera networks to use them as a source of localization data. Since the Cartesian position of the cameras in most networks is not known accurately, we consider the issue of how to localize such cameras. To solve this hybrid localization problem, we divide it into a local problem of camera-parameter estimation combined with a global planning and navigation problem. We solve the local camera-calibration problem by using fiducial markers attached to the robot and by selecting robot trajectories in front of each camera that provide good calibration and field-of-view accuracy. We propagate information among the cameras and the successive positions of the robot using an Extended Kalman filter. Finally, we move the robot between the camera positions to explore the network using heuristic exploration strategies. The paper includes experimental data from an indoor office environment as well as tests on simulated data sets.     

Estimation of the dynamic spinal forces using a recurrent fuzzy neural network.

Estimation of the dynamic spinal forces from kinematics data is very complicated because it involves the handling of the relationship between kinematic variables and electromyography (EMG) signals, as well as the relationship between EMG signals and the forces. A recurrent fuzzy neural network (RFNN) model is proposed to establish the kinematics-EMG-force relationship and model the dynamics of muscular activities. The EMG signals are used as an intermediate output and are fed back to the input layer. Since EMG is a direct reflection of muscular activities, the feedback of this model has a physical meaning. It expresses the dynamics of muscular activities in a straightforward way and takes advantage from the recurrent property. The trained model can then have the forces predicted directly from kinematic variables while bypassing the costly procedure of measuring EMG signals and avoiding the use of a biomechanics model. A learning algorithm is derived for the RFNN model.     

基于高清网络摄像机的自动聚焦算法

针对传统的聚焦评价函数运算量大、峰值搜索算法易受干扰且视频质量反复变化的问题,实现一种较传统聚焦评价函数与爬山算法相结合的聚焦算法更好的自动聚焦算法.采用TI公司的TMS320DM368处理器自带的自动聚焦(AF)引擎作为硬件实现的聚焦评价函数;采用的峰值搜索算法将聚焦曲线分为聚焦区和散焦区,在散焦区使用大步长的GM(1,1)模型预测搜索方向,在聚焦区使用小步长的爬山算法搜索峰值.实验结果表明,该算法拥有更好地实时性与准确性,适用于高清网络摄像机的自动聚焦.     

摄像头网络整体视域优化扩展模型及最优求解*

研究多摄像头网络环境下监控视域的最大化问题,进而提出全局性优化的粒子群算法获得最佳结果。研究通过调整摄像头的方向,相较于以往的同类系统进一步综合考虑单个摄像头节点的成像清晰度,以及网络的不同监视区域的重要性,从而提出更加适用更加全面的新模型。在这一新的模型下,定义了新的摄像头网络监视优度概念,基于此建立非线性优化方程。为解决这一非线性最大化问题,提出了采用全局优化方法的思路,并给出了以粒子群优化算法求解的具体方案。数值实验表明,该方案可以更好地解决摄像头网络的覆盖优化问题,无论是计算性能还是适用范围,相对于已有的文献和研究报告均有较大的改进。     

基于虚拟骨干网的MANET地址自动配置方法

针对MANET的地址自动配置问题,提出了基于虚拟骨干网的IPv6地址自动配置方法。充分利用了虚拟骨干网的分层结构、骨干网和子网分别执行不同的配置算法,消息复杂度和时间复杂度低,非常适合通信资源宝贵的MANET。     

Intrinsically dynamic network communities

Community finding algorithms for networks have recently been extended to dynamic data. Most of these recent methods aim at exhibiting community partitions from successive graph snapshots and thereafter connecting or smoothing these partitions using clever time-dependent features and sampling techniques. These approaches are nonetheless achieving longitudinal rather than dynamic community detection. We assume that communities are fundamentally defined by the repetition of interactions among a set of nodes over time. According to this definition, analyzing the data by considering successive snapshots induces a significant loss of information: we suggest that it blurs essentially dynamic phenomena—such as communities based on repeated inter-temporal interactions, nodes switching from a community to another across time, or the possibility that a community survives while its members are being integrally replaced over a longer time period. We propose a formalism which aims at tackling this issue in the context of time-directed datasets (such as citation networks), and present several illustrations of both empirical and synthetic dynamic networks. We eventually introduce intrinsically dynamic metrics to qualify temporal community structure and emphasize their possible role as an estimator of the quality of the community detection—taking into account the fact that various empirical contexts may call for distinct ‘community’ definitions and detection criteria.