面向入侵检测的改进BMHS模式匹配算法

模式匹配算法是入侵检测系统中使用较多的一种算法。分析几种常用模式匹配算法,提出一种基于BMHS的改进算法——N-BMHS算法。该算法对字符串进行双向模式匹配,引入一个新的数组s记录模式串中字符的出现次数,次数为1时可以利用已匹配的信息来辅助得到模式串的最大移动距离。实验结果表明,改进的模式匹配算法能减少比较次数,有效提高匹配速度。     

Linux文件加／解密过滤驱动的设计与实现

应用Linux内核中的系统调用劫持技术和模块动态装载机制,研究与开发Linux下的文件过滤驱动。通过对文件标识的注入与识别,采用blowfish加密算法,设计和实现内核级的文件加/解密服务,并对软件的性能进行分析。结果表明,在内核级增添软件功能,可以提高软件的运行效率,而计算机主机系统的安全性在内核机制的安全保障下得以增强。     

Error Analysis Methods for Group Decision Making Based on Hesitant Fuzzy Preference Relation

Hesitant fuzzy preference relation (HFPR) is an effective way to depict the decision makers’ preferences over the objects (alternatives or attributes) in the process of group decision making. Each component of the HFPR is characterized by several possible values and can express the decision makers’ hesitant information comprehensively. To make a decision with the HFPR, it is very necessary to find a proper technique for deriving the priority weights from the HFPR. In this paper, we use the error analysis as a tool to develop several straightforward methods for the priorities of the HFPR. We first define the expected value and the average value of each hesitant fuzzy element in the HFPR. Then based on the error analysis, we come up with the interval midpoint method, the average value method, and the difference method to derive the priority weights from the HFPR. After that, we discuss the relations among these methods, and utilize them and the possibility degree formula to develop an approach to decision making with the HFPR. Finally, we demonstrate the effectiveness and practicality of our approach through a case study concerning the investment problem in liquor enterprise.     

Automated extraction of fragments of Bayesian networks from textual sources

Mining large amounts of unstructured data for extracting meaningful, accurate, and actionable information, is at the core of a variety of research disciplines including computer science, mathematical and statistical modelling, as well as knowledge engineering. In particular, the ability to model complex scenarios based on unstructured datasets is an important step towards an integrated and accurate knowledge extraction approach. This would provide a significant insight in any decision making process driven by Big Data analysis activities. However, there are multiple challenges that need to be fully addressed in order to achieve this, especially when large and unstructured data sets are considered.In this article we propose and analyse a novel method to extract and build fragments of Bayesian networks (BNs) from unstructured large data sources. The results of our analysis show the potential of our approach, and highlight its accuracy and efficiency. More specifically, when compared with existing approaches, our method addresses specific challenges posed by the automated extraction of BNs with extensive applications to unstructured and highly dynamic data sources.The aim of this work is to advance the current state-of-the-art approaches to the automated extraction of BNs from unstructured datasets, which provide a versatile and powerful modelling framework to facilitate knowledge discovery in complex decision scenarios.     

基于GPU实现的AES加密

高级加密标准(Advanced Encryption Standard,AES),在密码学中又称Rijndael加密法,是美国联邦政府采用的一种区块加密标准。该算法已经被多方分析论证并广为全世界所使用。传统的AES加密运算是在CPU上实现的,现在为了提高加密速度以处理大规模的加密运算,文中提出了一种在图像处理器(Graphics Processing Unit)上实现AES加密算法的方法。该方法的实现有两种,一种是基于传统OpenGL的AES实现,另一种是基于最新技术CUDA的AES实现。文中阐述的是前者。经过测试,该方法比传统CPU的实现提高了15到40倍左右的速度。     

A priori and a posteriori analysis for a locking-free low order quadrilateral hybrid finite element for Reissner–Mindlin plates

This paper proposes a quadrilateral finite element method of the lowest order for Reissner–Mindlin (R–M) plates on the basis of Hellinger–Reissner variational principle, which includes variables of displacements, shear stresses and bending moments. This method uses continuous piecewise isoparametric bilinear interpolation for the approximation of transverse displacement and rotation. The piecewise-independent shear stress/bending moment approximation is constructed by following a self-equilibrium criterion and a shear-stress-enhanced condition. A priori and reliable a posteriori error estimates are derived and shown to be uniform with respect to the plate thickness t. Numerical experiments confirm the theoretical results.     

基于无线传感器网络的Modbus虚拟设备

为有机结合无线传感器网络(WSN)和Modbus现场总线网络,提出了一种新的解决方案,利用网关的协议转换功能,将无线传感器网络节点虚拟成Modbus设备,从而扩展了Modbus协议的使用范围。该方案的优点在于不增加无线传感器网络节点的开销实现无线传感器网络和Modbus总线的无缝集成,同时保证了无线传感器网络响应Modbus查询时的实时性。仿真实验证明了该方案的可行性。     

基于干预规则挖掘因果关系与分段干预事件及对比不等式

为揭示亚复杂系统(SCS)在干预下的特殊行为,提出了对比不等式概念,提出并实现了分段干预规则的挖掘算法;通过在亚复杂系统中施加扰动式干预并分析其动力学效应,提出并实现了基于扰动因果关系挖掘算法。在真实数据上的实验表明,分段干预规则发现了传统挖掘方法难以发现的新干预事件,因果挖掘算法发现了传统方法难以发现的因果关系。     

支持向量回归预测蛋白质残基的B因子

蛋白质分子的柔性在各种生物进程中如酶催化、蛋白质分子绑定和识别等发挥着重要的作用。研究蛋白质分子柔性有助于更好地理解蛋白质的功能,研究证明从X射线晶体衍射结构而来的B因子能够较好的衡量蛋白质分子的柔性。从蛋白质序列出发,以保守信息、预测的二级结构、预测的相对溶剂可及性和氨基酸4种物理化学性质作为特征向量,利用支持向量回归方法对蛋白质的B因子进行预测。考察了邻近残基的影响,采用滑动窗口方法对所有特征变量进行了处理。通过对位置特异得分矩阵的平滑窗口处理,提高了预测精度。以皮尔逊相关系数为算法评价指标,最终得到独立测试集的皮尔逊相关系数为0.56,表明该方法有效提高预测精度。     

Web恶意代码主动检测与分析系统的设计与实现

在深入研究了客户端蜜罐的基础上,提出了动态与静态相结合的Web恶意代码检测方法,实现了Web恶意代码主动检测与分析系统(HoneyCat).该系统主动对指定的网站进行检测,并对可疑的页面进行分析,通过动态跟踪检测IE进程对注册表和文件的操作以及其网络行为,发现是否存在可疑行为,然后对有可疑行为的网页进行静态分析.静态分析利用漏洞特征库定位恶意代码的准确位置和所利用的漏洞.对于无法识别所利用漏洞的页面生成一个分析文件,为手工分析提供帮助,有助于对漏洞的研究,并有机会发掘未知漏洞.经过测试发现该系统运行稳定,准确率高,能有效地检测出页面中的恶意代码.