Neuro-fuzzy system modeling based on automatic fuzzy clustering

A neuro-fuzzy system model based on automatic fuzzy dustering is proposed. A hybrid model identification algorithm is also developed to decide the model structure and model parameters. The algorithm mainly includes three parts：1） Automatic fuzzy C-means （AFCM）, which is applied to generate fuzzy rttles automatically, and then fix on the size of the neuro-fuzzy network, by which the complexity of system design is reducesd greatly at the price of the fitting capability; 2） R.ecursive least square estimation （RLSE）. It is used to update the parameters of Takagi-Sugeno model, which is employed to describe the behavior of the system;3） Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network. Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.     

Maximum power point tracking （MPPT） control of a photovoltaic system based on dual carrier chaotic search

The output power of the photovoltaic (PV) array changes with the change in external environment and load.Therefore,maximum power point tracking (MPPT) technology is needed to maximize the efficiency of...     

Research of internet worm warning system

In this paper, by analyzing the worm’s propagation model, we propose a new worm warning system based on the method of system identification, and use recursive least squares algorithm to estimate the worm’s infection rate. The simulation result shows the method we adopted is an efficient way to conduct Internet worm warning.     

Research of internet worm warning system based on system identification

The frequent explosion of Internet worms has been one of the most serious problems in cyberspace security.In this paper, by analyzing the worm's propagation model, we propose a new worm warning system based on the method of system identification, and use recursive least squares algorithm to estimate the worm's infection rate. The simulation result shows the method we adopted is an efficient way to conduct Internet worm warning.     

基于贝叶斯的网络蠕虫检测技术的研究

介绍了网络蠕虫的定义和工作机制,通过分析得到了网络蠕虫的共有特性.根据蠕虫的特性,提出了一个基于贝叶斯的网络蠕虫检测方法,采用贝叶斯公式来计算首次连接失败的概率,对于不能确定的情况,用后验概率更新先验概率.设计了一个基于贝叶斯的网络蠕虫检测系统的原型系统,详细描述了该系统的结构模型,并从功能角度上介绍了该系统的两个主要组成部分.     

面向对象的蠕虫仿真方法研究

本文首先分析了网络蠕虫的特征行为,从宏观和微观两个层次对蠕虫进行了研究,并指出借助仿真实验来研究蠕虫的方法的优势所在;然后通过对仿真环境的构造的分析和对仿真动力来源的介绍,详细阐述了蠕虫仿真的基本过程;最后给出面向对象的网络蠕虫仿真的基本思路与方法。     

新型工业控制系统勒索蠕虫威胁与防御

工业控制系统（ICS）的大规模攻击对于电力生产、输配电、石油化工、水处理和传输等涉及国计民生的关键基础设施是一个巨大的威胁,目前提出的针对ICS的勒索蠕虫受限于工控网络隔离的特性,难以大规模传播。基于观察到的ICS实际开发场景,针对ICS高度隔离化的问题,提出一种基于新的攻击路径的勒索蠕虫威胁模型。此威胁模型首先将工程师站作为初次感染目标,然后以工程师站作为跳板,对处于内部网络的工业控制设备进行攻击,最后实现蠕虫式感染和勒索。基于此威胁模型,实现了ICSGhost——一种勒索蠕虫原型。在封闭的实验环境中,ICSGhost能够以预设的攻击路径对ICS进行蠕虫式感染;同时,针对该勒索蠕虫威胁,讨论了防御方案。实验结果表明此种威胁切实存在,并且由于其传播路径基于ICS实际的开发场景,较难检测和防范。     

A new algorithm for recursive estimation of parameters in controlled ARMA processes

A recursive algorithm for estimating the constant but unknown parameters of a controlled ARMA process is presented. The algorithm is a recursive version of an off-line algorithm using three stages of standard least-squares. In the first stage the parameters of a controlled AR model of degree p are estimated. The residuals used in this stage are employed in the second stage to estimate the parameters of a controlled ARMA process. The first two stages constitute a recursive version of Durbin's algorithm. The model obtained in the second stage is used to filter the input, output and residuals and these filtered variables are used in the final stage to obtain improved estimates of the controlled ARMA process. It is shown that the estimate is (globally) p-consistent, i.e. that the estimate converges a.s. as the number of data tends to infinity, to a vector which, in turn, converges to the true parameter vector as the degree p of the AR model tends to infinity.     

基于良性蠕虫对抗P2P蠕虫的策略研究

随着P2P软件的推广与普及, P2P蠕虫已成为P2P网络安全的主要威胁之一,P2P蠕虫基于hit-list攻击邻居节点,具有隐蔽性好、攻击性强等特点。基于混合型良性蠕虫概念设计出自动优先趋进优质节点(APTHQN)的对抗策略,合理利用优质节点的拓扑优势,有效适应了P2P网络的动态性。实验仿真结果表明,采用此策略的良性蠕虫在保持较少的网络资源消耗同时,高效地抑制了P2P蠕虫的传播。     

基于正交最小二乘法的小波网络在系统辨识中的应用

利用单尺度小液框架理论建立初始小波网络。首先，提出基于ROLS思想的小波网络正交化算法，实现参数的在线辩识。其次，提出一种基于0LS思想的小波网络结构辩识算法，删除那些所网络影响小的小波基，来解决高维辩识问题。最后，列举事例证明了所述方法的有效性。     

网络蠕虫与IDS防范

蠕虫的爆发给计算机用户带来巨大的损失,蠕虫的防范工作变得尤为重要。本文从蠕虫和IDS的相关技术出发,简要分析蠕虫爆发时IDS的网络特征,进而提出一个行之有效的蠕虫检测模型结构;最后提出在此领域未来的研究发展方向,为今后的科研工作提出新的研究思路。     

网络蠕虫爆发的检测算法及其应用

新一代的网络蠕虫融合了病毒、木马、DDOS攻击等各种攻击手段,一旦爆发,将迅速导致大规模的网络阻塞甚至瘫痪。提出了一种蠕虫爆发检测算法,通过对网络流量变化率的监测,可以在蠕虫爆发的前期发现网络异常,从而使网络管理员和应急响应组织获得更多的反应时间,在蠕虫阻塞网络之前采取措施。算法使用DARPA98入侵检测评估系统进行了评估,并应用于Blaster、Nachi、slammer和Sasser蠕虫爆发时的真实数据。