基于AS关系的BGP无效路由检测

由于BGP(Border Gateway Protocol)路由配置错误和网络安全问题会导致无效的路由产生,影响Inter-net性能。为此,提出了一种BGP无效路由检测方法。该方法通过对入境和出境路由的地址前缀根据IP地址分配情况和所属关系检测来确定所发布前缀的合法性,发现无效的地址前缀。依据自治系统之间的商业关系,通过分析出入境路由的AS(Autonomous System)路径属性发现无效的AS路径。所给方法能有效地发现Inter-net上BGP配置错误所产生的无效路由,并限制这些无效路由在Internet上的扩散,提高了Internet整体性能。

Practical Security Approaches against Border Gateway Protocol (BGP) Session Hijacking Attacks between Autonomous Systems

The border gateway protocol (BGP) is the default inter domain routing protocol used on the internet for exchanging information between autonomous systems. Available literature suggests that BGP is vulnerable to session hijacking attacks. There are a number of proposals aimed at improving BGP security which have not been fully implemented. This paper examines a number of approaches for securing BGP through a comparative study and identifies the reasons why these proposals have not been implemented commercially. This paper analyses the architecture of internet routing and the design of BGP while focusing on the problem of BGP session hijacking attacks. Using Graphical Network Simulator 3 (GNS-3), a session hijack is demonstrated and a solution which involves the implementation of route filtering, policy-maps and route-maps on CISCO routers representing ASes is carried out. In the end, a workable industry standard framework for securing and protecting BGP sessions and border routers from exploitation with little or no modification to the existing routing infrastructure is demonstrated.

验证BGP的AS间路由转发及同步

边界网关协议BGP适合在多个AS自治系统间交换路由信息,对运营管理系统的集成和协调起着重要作用,通常对配置多个AS之间的路由转发及IBGP的路由同步存在诸多问题。通过搭建多自治系统的实验环境,在BGP网络中采用路由重发布、内部邻居设定、指定IBGP的next-hop、启用路由同步、路由汇总等方法,给出了针对所存在问题的解决方案并给予了论证。

基于BGP协议的AS级网络拓扑测量的研究

在分析Internet结构的基础上,主要针对BGP协议在AS级拓扑测量中的应用做了深入的讨论.介绍了自治系统的概念和BGP协议的工作过程,描述了监测运行有BGP协议路由器的路由表以动态构造AS级网络拓扑图的原理,给出了基于BGP协议的AS级网络拓扑测量的算法,并对算法进行了说明.

RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

BGP路由黑洞分析与仿真

作为运行与自治系统间的路由协议,BGP路由中的AS_PATH属性避免了环路的出现,但是也带来了路由黑洞问题.通过分析BGP的工作原理深入了解路由黑洞产生的原因,通过查阅文献,对提出的四种路由黑洞解决方案逐一分析其设计思路,在此基础上提出一种基于MPLS协议解决BGP路由黑洞的方案,详细分析MPLS的工作原理并在仿真平台中进行模拟配置,验证其可行性.最后通过各方案在资源占用、兼容性、配置复杂度、适用网络规模等方面的对比,论证了方案的优势.

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

Design and Implementation of a Multi-Sensor Based Object Detecting and Removing Autonomous Robot Exploration System

Developing autonomous mobile robot system has been a hot topic in AI area. With recent advances in technology, autonomous robots are attracting more and more attention worldwide, and there are a lot of ongoing research and development activities in both industry and academia. In complex ground environment, obstacles positions are uncertain. Path finding for robots in such environment is very hot issues currently. In this paper, we present the design and implementation of a multi-sensor based object detecting and moving autonomous robot exploration system, 4RE, with the VEX robotics design system. With the goals of object detecting and removing in complex ground environment with different obstacles, a novel object detecting and removing algorithms is proposed and implemented. Experimental results indicate that our robot system with our object detecting and removing algorithm can effectively detect the obstacles on the path and remove them in complex ground environment and avoid collision with the obstacles.

SE-BGP:一种BGP安全机制

BGP(bordergatewayprotocol)协议的安全是Internet路由系统安全的关键.目前已提出多种BGP安全机制,但都未能得到部署.对BGP安全机制的部署问题进行深入分析,利用AS(autonomous system)结构的Rich-Club特性,提出AS联盟的概念,设计了一种BGP安全机制:SE-BGP(security enhanced BGP).SE-BGP采用基于AS联盟的安全体系结构,使用一种具有分布式认证中心的新的信任模型——TTM(translato rtrust model).设计了基于TTM模型的认证算法,给出了基于现有BGP协议的扩充实现方法.与已有的安全机制相比,SE-BGP在保证安全能力的同时,所需的证书规模大约为原有机制的1%,具有良好的可扩展性.

基于SimHash算法的主机网络嗅探攻击自主检测方法

目前提出的主机网络嗅探攻击自主检测方法转发包数据过高,导致嗅探攻击成本过低,难以在短时间内实现嗅探攻击检测。为了解决上述问题,以SimHash算法作为检测核心,提出了一种新的主机网络嗅探攻击自主检测方法。嗅探器在捕获主机网络系统内通信信息时,利用通信链路的特点和算法,建立三个约束条件用于选择最佳通信链路的最佳路径,通过路径剔除确定主机内非法信息的通信路径,确保嗅探器获取通信链路信息的真实性。计算比特流向量序列,检测数据错误包,通过数据信息的切割提高嗅探攻击成本,实现了主机网络嗅探攻击自主检测。实验结果表明,基于SimHash算法的主机网络嗅探攻击自主检测方法能够提高嗅探攻击成本,在短时间内实现主机网络嗅探攻击自主检测。

Generating routes for autonomous driving in vehicle-to-infrastructure communications

The study of vehicular networks has attracted considerable interest in academia and the industry.In the broad area,connected vehicles and autonomous driving are technologies based on wireless data communication between vehicles or between vehicles and infrastructures.A Vehicle-to-Infrastructure(V2I)system consists of communications and computing over vehicles and related infrastructures.In such a system,wireless sensors are installed in some selected points along roads or driving areas.In autonomous driving,it is crucial for a vehicle to figure out the ideal routes by the communications between its equipped sensors and infrastructures then the vehicle is automatically moving along the routes.In this paper,we propose a Bezier curve based recursive algorithm,which effectively creates routes for vehicles through the communication between the On-Board Unit(OBU)and the Road-Side Units(RSUs).In addition,this approach generates a very low overhead.We conduct simulations to test the proposed algorithm in various situations.The experiment results demonstrate that our algorithm creates almost ideal routes.

Labeling algorithm and its fairness analysis for autonomous system

A kind of packet labeling algorithm for autonomous system is introduced. The fairness of the algorithm for each traffic stream in the integrated-services is analyzed. It is shown that the rate of each stream in the integrated-services would converge to a stable value if the transmittfing or forwarding rates converge to that of the receiving exponentially.

Graph-based robot optimal path planning with bio-inspired algorithms

Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal solution.This limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy efficiency.To address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains.The resulting mDPderived graph is then modeled using a Maklink graph theory.By applying the iSOA approach,the trajectory of an autonomous robot in the workspace is optimized.Additionally,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints.The proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art algorithms.The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.

Efficiency-oriented vehicle relocation of shared autonomous electric fleet in station-based car-sharing system

Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators.To address the problems above,a“demand forecast-station status judgement-vehicle relocation”multistage dynamic relocation algorithm based on the automatic formation cruising technology was proposed in this study.In stage one,a novel trip demand forecast model based on the long short-term memory network was established to predict users'car-pickup and car-return order volumes at each station.In stage two,a dynamic threshold interval was determined by combining the forecast results with the actual vehicle distribution among stations to evaluate the status of each station.Then vehicle-surplus,vehicleinsufficient,vehicle-normal stations,and the number of surplus or insufficient vehicles for each station were counted.In stage three,setting driving mileage and carbon emission as the optimization objectives,an integer linear programming mathematical model was constructed and the optimal vehicle relocation scheme was obtained by the commercial solver Gurobi.Setting 43 stations and 187 vehicles in Jiading District,Shanghai,China,as a case study,results showed that rapid vehicle rebalancing among stations with minimum carbon emissions could be realized within 15 min and the users’car-pickup and car-return demands could be fully satisfied without any refusal.

边界网关协议BGP-4中路由信息的存储合并算法研究

针对BGP-4的AS路径和NEXT_HOP两个基本路由属性的特点逐步提出了一系列哈希算法，并采用真实的网络数据对其效果进行了检验。然后优选出一种算法分析了空间和时间性能。最后提出了路由的合并规则。

一种BGP路由配置错误动态检测方法

针对BGP路由源配置错误和路由输出配置错误,提出了一种BGP路由配置错误动态检查方法。该方法通过对入境和出境路由的地址前缀进行所属关系检测来确定所声明前缀的合法性,发现路由源配置错误。依据自治系统之间的商业关系,通过分析出入境路由的AS路径属性来发现违反路由输出原则的路由输出配置错误。通过仿真实验证明了所给方法的正确性和可行性。

Application of GA, PSO, and ACO Algorithms to Path Planning of Autonomous Underwater Vehicles

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem （NOCP）. An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem （TPBVP）. A genetic algorithm （GA）, particle swarm optimization （PSO）, and ant colony optimization （ACO） algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Autonomous optical navigation for interplanetary exploration based on information of earth-moon

The image elements of earth-center and moon-center are obtained by processing the images of earthand moon, these image elements in combination with the inertial attitude information and the moon ephemerisare utilized to obtain the probe initial position relative to earth, and the Levenberg-Marquardt algorithm is usedto determine the accurate probe position relative to earth, and the probe orbit relative to earth is estimated by u-sing the extended Kalman filter. The autonomous optical navigation algorithm is validated using the digital simu-lation.

Dynamic A^*path finding algorithm and 3D lidar based obstacle avoidance strategy for autonomous vehicles

This paper presents a novel dynamic A^*path finding algorithm and 3D lidar based local obstacle avoidance strategy for an autonomous vehicle.3D point cloud data is collected and analyzed in real time.Local obstacles are detected online and a 2D local obstacle grid map is constructed at 10 Hz/s.The A^*path finding algorithm is employed to generate a local path in this local obstacle grid map by considering both the target position and obstacles.The vehicle avoids obstacles under the guidance of the generated local path.Experiment results have shown the effectiveness of the obstacle avoidance navigation algorithm proposed.