基于地域通信网的流量攻击方法研究

在野战地域通信网中 ,无线链路占有较大的比例。而这些系统中的节点都需要保持相互的通信联络。为此 ,从通信链路攻击野战地域通信网系统的可行性比较大。本文主要介绍了流量控制、流量攻击 ,并提出了流量攻击的一些方法     

基于地域通信网的流量攻击方法研究

在野战地域通信网中，无线链路占有较大的比例。而这些系统中的节点都需要保持相互的通信联络。为此，从通信链路攻击野战地域通信网系统的可行性比较大。本文主要介绍了流量控制、流量攻击，并提出了流量攻击的一些方法。     

一种基于神经网络的通信网节点重要性评价方法

准确地评估通信网中节点的重要性,对于通信网的设计、维护管理以及提高整个通信网的可靠性都有重要作用。现有的一些评估方法通常只是考虑单一的影响因素,其评估结果不准确。提出一种基于自组织神经网络的通信网节点重要性评估模型,将通信网中节点的度、节点的网络凝聚度、节点对通信网生成树的影响程度和节点对通信网可靠性的影响程度作为节点重要性的评价指标,对通信网中的节点进行重要性等级的划分。实验结果表明该模型能够全面地评估节点的重要性。     

基于聚合度的地域通信网关键节点识别方法

分析了地域通信网的网络模型,从网络的拓扑结构出发,以图论知识为基础,把节点的度数和聚合度作为衡量节点重要性的标准,给出了关键节点的定义方法,通过分析对比得出地域通信网中有些关键节点并不具有较大的度分布特征,而且链路之间存在着一定的差别,采用度作为节点重要性的评估方法具有一定的片面性。在此基础上,文中提出了一种基于聚合度大小排序的关键节点识别方法,并给出了关键节点识别流程。实例分析结果表明了该方法的有效性、简单性和准确性。     

基于改进跳面节点法的地域通信网抗毁性评价

为了提高地域通信网对抗训练的有效性,需要对地域通信网的抗毁性和节点链路的重要性作出评价。针对跳面节点法的问题和不足,提出了一种基于改进跳面节点法的地域通信网抗毁性评价方法,并对改进的结果进行了对比,最后对抗毁性评价方法在地域通信网对抗训练中的应用情况进行了分析。     

地域通信网干扰效果评估指标研究

目前地域通信网是战术通信网的主体和主要发展方向。介绍了地域通信网的特点,分析了对地域通信网的干扰方法。针对地域通信网的工作特性,提出了采用误码率和网络阻塞概率作为对地域网干线节点干扰的效果评估指标,最后以四个节点组成的栅格状地域通信网作为研究对象,采用数学仿真方法对多种情况下的误码率、网络阻塞概率进行了仿真评估。     

基于地域通信网流量攻击方法的仿真研究

地域通信网中各节点之间的正常通信是保障作战胜利的关键,由于地域通信网中各节点间的连接多采用无线链路,可以使用流量攻击的方法对链路进行攻击,使各节点通信中断。在分析流量控制和拥塞控制的基础上,结合OPNET仿真软件建立网络模型,通过仿真研究验证了流量攻击法可以有效地使地域通信网通信中断,达到攻击的目的。     

地域通信网链路威胁评估研究

链路威胁评估是进行干扰资源分配的基础.针对目前新型干扰技术出现的新情况和对网络重要链路实施干扰的新需要,首先分析了现阶段针对地域通信网的3类干扰技术,在此基础上,给出了通信网络环境下评估链路威胁的指标体系,并采用基于路径的方法对链路战术重要性进行了计算,最后运用多属性决策方法在具体网络场景下对链路威胁等级进行了评估计算...     

野战地域通信网可靠性的评价方法

本文针对野战地域通信网网络拓扑复杂并随战场环境发生变化的特点,提出了一种快速评价野战地域通信网可靠性的方法,给出了可靠性定量计算的数学解析式,该方法简单、直观,能反映网络拓扑的可靠性和抗毁性。     

聚合度在地域通信网关键节点识别中的应用

介绍了地域通信网的概念,分析了研究地域通信网关键节点识别的重要性。从网络的拓扑结构出发,给出了两种聚合度的定义方法,并把这两种定义方法应用到关键节点识别中,通过实例对两种方法进行分析对比它们的优缺点,得出方法二更适用于地域通信网这样复杂网络的关键节点识别中。     

A linear-time algorithm to compute the reliability of planarcube-free networks

A planar graph G=(V,E) is a cube-free graph (CF graph) if it has no subgraph homomorphic to the cube. The cube is the graph whose vertices and edges are the vertices and edges of the three dimensional geometric cube. The all-terminal reliability of a graph G whose edges can fail (with known probabilities) is the probability that G is connected. The problem of computing the all-terminal reliability of a general graph is NP-hard. An O(| V|) time algorithm for computing the all-terminal reliability of CF graphs is presented     

Covering properties of convolutional codes and associated lattices

The paper describes Markov methods for analyzing the expected and worst case performance of sequence-based methods of quantization. We suppose that the quantization algorithm is dynamic programming, where the current step depends on a vector of path metrics, which we call a metric function. Our principal objective is a concise representation of these metric functions and the possible trajectories of the dynamic programming algorithm. We shall consider quantization of equiprobable binary data using a convolutional code. Here the additive group of the code splits the set of metric functions into a finite collection of subsets. The subsets form the vertices of a directed graph, where edges are labeled by aggregate incremental increases in mean squared error (MSE). Paths in this graph correspond both to trajectories of the Viterbi algorithm and to cosets of the code. For the rate 1/2 convolutional code [1+D², 1+D+D²], this graph has only nine vertices. In this case it is particularly simple to calculate per dimension expected and worst case MSE, and performance is slightly better than the binary [24, 12] Golay code. Our methods also apply to quantization of arbitrary symmetric probability distributions on [0, 1] using convolutional codes. For the uniform distribution on [0, 1], the expected MSE is the second moment of the “Voronoi region” of an infinite-dimensional lattice determined by the convolutional code. It may also be interpreted as an increase in the reliability of a transmission scheme obtained by nonequiprobable signaling. For certain convolutional codes we obtain a formula for expected MSE that depends only on the distribution of differences for a single pair of path metrics     

Maximizing the Mean Number of Communicating Vertex Pairs in Series-Parallel Networks

A communication network can be modelled as a probabilistic graph where each of b edges represents a communication line and each of n vertices represents a communication processor. Each edge e (vertex v) functions with probability Pe (pv). If edges fail independently with uniform probability p and vertices do not fail, the probability that the network is connected is the probabilistic connectedness and is a standard measure of network reliability. The most reliable maximal series-parallel networks by this measure are those with exactly two vertices of degree two. However, as p becomes small, or n becomes large, the probability that even the most reliable series-parallel network is connected falls very quickly. Therefore, we wish to optimize a network with respect to another reliability measure, mean number of communicating vertex pairs. Experimental results suggest that this measure varies with p, with the diameter of the network, and with the number of minimum edge cutsets. We show that for large p, the most reliable series-parallel network must have the fewest minimum edge cutsets and for small p, the most reliable network must have maximum pairs of adjacent edges. We present a construction which incrementally inproves the communicating vertex pair mean for many networks and demonstrates that a fan maximizes this measure over maximal series parallel networks with exactly two edge cutsets of size two.     

一种计算Ad hoc网络K-终端可靠性的线性时间算法

研究计算Ad hoe网络K-终端可靠性的线性时间算法,可以快速计算Ad hoe网络K-终端可靠性。为了计算Ad hoe网络分级结构尽终端可靠性,可以采用无向概率图表示Ad hoe网络的分级结构。每个簇头由已知失效率的结点表示,并且当且仅当两个簇相邻时,两个结点间的互连由边表示。这个概率图的链路完全可靠,并且已知结点的失效率。此图的K-终端可靠性为给定K-结点集是互连的概率。文中提出了基于合适区间图计算尽终端可靠性的一种线性时间算法。本算法可用来计算Ad hoe网络的K-终端可靠性。其时间复杂度为O（｜V｜＋｜E｜）。     

Factoring and reductions for networks with imperfect vertices

Factoring and reductions are effective methods for computing the K-terminal reliability of undirected networks, but they have been applied mostly to networks with perfect vertices. However, in real problems, vertices may fail as well as edges. Imperfect vertices can be factored like edges, but the complexity then increases exponentially with their number. A technique has been developed to account for the failure of vertices with small additional cost, using a modified method of factoring and reductions. This technique is very easy to integrate into a factoring algorithm. It consists of factoring not on a single element (e.g., a single edge) but on a set of elements (e.g., an edge and its endpoints). The problem is that random variables associated with the elements of the network are no longer independent. This can be handled by choosing factoring edges that have at least one perfect endpoint. This technique leaves the factoring algorithm practically unchanged. The only difference is that some supplementary probability values are kept for the imperfect vertices of the original and the induced graphs. For algorithms using simple reductions, it has negligible computational cost     

A linear-time algorithm for computing K-terminal reliability onproper interval graphs

Consider a probabilistic graph in which the edges are perfectly reliable, but vertices can fail with some known probabilities. The K-terminal reliability of this graph is the probability that a given set of vertices K is connected. This reliability problem is #P-complete for general graphs, and remains #P-complete for chordal graphs and comparability graphs. This paper presents a linear-time algorithm for computing K-terminal reliability on proper interval graphs. A graph G = (V, E) is a proper interval graph if there exists a mapping from V to a class of intervals I of the real line with the properties that two vertices in G are adjacent if their corresponding intervals overlap and no interval in I properly contains another. This algorithm can be implemented in O(|V| + |E|) time     

On the reliability of a number of s-p reducible graphs

Let G=(V,E) be a graph whose edges may fail with known probabilities and let K a subset of V be specified. The overall reliability of G, denoted by R(G), is the probability that all vertices in K=V communicate with each other. We have two types of graphs, s-p reducible and s-p complex, depending on whether after series-arallel reductions the result is a single edge or not. A number of s-p reducible graphs are presented and expressions that evaluate their overall reliability are introduced.     

Factoring Algorithms for Computing K-Terminal Network Reliability

Let GK denote a graph G whose edges can fail and with a set K ? V specified. Edge failures are independent and have known probabilities. The K-terminal reliability of GK, R(GK), is the probability that all vertices in K are connected by working edges. A factoring algorithm for computing network reliability recursively applies the formula R(GK) = piR(GK * ei) + qiR(GK - ei) where GK * ei is GK, with edge ei contracted, GK - ei is GK with ei deleted and pi ? 1 - qi is the reliability of edge ei. Various reliability-preserving reductions can be performed after each factoring operation in order to reduce computation. A unified framework is provided for complexity analysis and for determining optimal factoring strategies. Recent results are reviewed and extended within this framework.     

边失效概率不同情况下网络全终端可靠度的近似计算

用可靠性多项式计算网络全终端可靠度是评估网络拓扑结构德定程度的重要依据,精确计算可靠性多项式是一个NP-hard问题.本文通过对基于产生孤立点的概率随机事件的定义,运用概率不等式变换,给出了点可靠、边以不同的概率相互独立失效时,网络全终端可靠度的上界表达式,在可靠度近似计算过程中避免了对网络边割集和路集的搜索.最后,在...