元数据相关推理研究

在多级安全数据库中,推理通道的存在会对信息的安全造成威胁。在推理问题中,与元数据相关的推理是其中的一个重要方面,对该问题的研究有助于数据库的安全增强。将与元数据相关的推理问题进一步划分为不同类别描述,对不同类别的推理问题,分别探讨了如何进行控制,并给出了解决的方法。为了消除推理通道,需要修改属性的安全级别标识信息;提出了一个标识修改的模型MTL,用于通过修改属性安全标识来消除推理通道。     

一种基于概念图的推理问题解决方法

本文使用概念图为数据库的推理问题建立了相关的解决模型,给出了推理相关的知识 表示及推理过程的描述,建立了相关的推理控制系统。     

概念格的内涵缩减与数据库推理依赖

值依赖是数据库推理问题研究的一个新课题.首先介绍了形式背景和概念格,提出了值依赖的形式概念模型.将数据属性的安全敏感级别引入到值依赖研究中,提出推理依赖及α极大推理依赖概念,并建立了形式概念格的内涵缩减与数据库推理依赖之间的关系.进一步证明了由概念格的内涵缩减推导出数据库中完备的、无冗余的α极大推理依赖集.最后提出并实例验证了发现数据库中全部推理依赖集的算法.推理依赖是关系数据库中最重要的属性依赖关系之一,其研究对检测和消除数据库推理通道具有十分重要的意义.     

基于推理通道的函数依赖推理控制

数据库推理控制是数据库安全领域的重要研究内容.函数依赖关系在推理攻击中通常会起到重要作用.文中对基于函数依赖关系的推理控制问题进行了研究,在推理通道思想的基础上,提出了一种基于最小函数依赖集构造推理通道的方法,以及一种基于推理通道的动态组合通道实例的方法,实现了在数据库运行阶段针对函数依赖关系的动态推理控制.上述方法已应用于国产数据库神舟OSCAR安全版中,实践表明,上述方法能够有效地防御基于函数依赖关系的推理攻击.     

多级安全数据库系统的推理分析

介绍了多级安全数据库系统的推理问题,及用于推理的信息来源和方法策略;研究并分析了目前在多级安全数据库系统中推理问题的成果：推理的形式化,数据库设计中的推理控制技术,数据库系统运行时的推理控制方法,以及数据级的推理控制方法,并指出了各个方法的局限性.     

基于粗糙集的推理通道消除

基于粗糙集理论对推理通道问题进行了研究。通过采用属性约简和属性值约简方法对数据库中的数据进行处理。在属性值约简基础之上,采用一种改进算法找出了数据库中推理规则集。进一步,将推理规则集中属性频率高的属性安全级别提高至决策属性的安全级别,从而消除推理通道。最后通过一个实例表明提出的消除通道算法是有效的。     

数据库弱函数依赖推理规则挖掘方法

数据库推理问题是数据库安全研究的重要分支方向,推理通道的分析是解决推理问题的基础.传统推理规则无法处理不完全符合函数依赖的数据关系,针对这一问题,本文提出了弱函数依赖的概念,并给出了弱函数依赖的推理能力评价标准,最后给出了从数据中挖掘弱函数依赖关系的算法.     

在INGRES上实现的一个演绎系统

演绎数据库是将推理机制引入传统关系数据库而生成的一种新型数据库形式,它是数据库领域中一个比较新的方向。实现演绎数据库的主要问题是推理规则的表示和推理过程的实现。其中,演绎推理的有效实现是演绎数据库实现的关键。目前,实现演绎数据库的途径有两个:一个是直接用逻辑程序设计语言PROLOG来实现;另一个是在传统关系数据库     

基于数据挖掘的数据库安全技术研究

随着数据库技术的快速发展,数据挖掘成为从海量数据中发现有价值信息的一种有效方法。数据挖掘为人们使用数据库带来了方便的同时,也带来了许多安全问题,其会导致数据库系统产生推理和聚集攻击。本文详细的介绍了数据挖掘对数据库系统安全带来的影响,同时针对系统受到的推力和聚集攻击提出了一种基于归纳和演绎的混合推理策略,防止数据库发生信息泄露等安全问题。     

一种基于粗糙集的多级安全数据库推理问题的量化分析方法

信息大都存放在数据库中,信息安全很大程度上依赖数据库的安全。推理是从已知的信息推出新的信息,它是对数据库安全的一个重要威胁,特别数据挖掘的发展,这个威胁变得更加严重。简单描述了多级安全数据库系统的推理问题;介绍了粗糙集理论的相关概念;利用粗糙集理论提出了一种对推理风险进行量化的方法,它不依赖系统安全员的知识,能够处理确定性和非确定性的推理通道;并给出了一个分析例子。     

对等数据管理系统中数据映射推导问题的研究

映射表是对等数据管理系统广泛使用的一种数据映射工具。通过构造大量新的映射关系,映射推导技术能极大地提高数据共享程度。已有的映射推导研究集中于理论分析,该文在探讨映射推导问题的基础上,从用户角度出发设计了一个映射关系模型,在此基础上,给出了数据映射推导系统的总体框架。     

基于数据立方体的动态推理控制方法

基于数据立方体的静态推理控制方法在联机分析处理(OLAP)系统中的访问有效性不高。为此,提出一种基于数据立方体的动态推理控制方法。该方法以提高OLAP系统访问有效性为目的,实时处理在线查询,分析查询方体的推理威胁,阻止其推理产生,并动态返回可访问方体集。实验结果表明,该方法可提高推理系统的有效性,且与静态推理控制方法有相同的安全性。     

Logical Conditioning and Entropy Inference Based on Observational Data

The logical conditioning inference problem is studied when a simple condition setting a threshold for a potential observation of a scalar observable uncertain quantity is introduced as an additional information to the pieces of evidence originally available to make inference, which include observational data. Two alternative ways of incorporating this condition can be considered. They lead to two conditioned evidences from which it is possible to start and, consequently, two different inference problems arise. Due to the need to coherently represent observational data and to express unambiguously the given evidences, these problems are formalized in a plausible logic language with observational data, within the logical probability framework. They are solved by applying the relative entropy method with fractile constraints. A comparison of the solutions obtained indicates that one of them is a particular instance of the other. It is concluded that the broadest one constitutes the general solution to the logical conditioning inference problem.     

A Linear-Time Algorithm for the Perfect Phylogeny Haplotype Problem

The inference of evolutionary trees from binary species-character matrices is a fundamental computational problem in classical phylogenetic studies. Several problems arising in this field lead to different variants of the inference problem; some of these concern input data with missing values or incomplete matrices. A model of inference from incomplete data that has recently gained a remarkable interest is the Perfect Phylogeny Haplotype problem (PPH) introduced in [1] and successfully applied to infer haplotypes from genotype data. A stated open issue in this research field is the linear-time solution of this inference problem. In this paper we solve this question and give an O(nm)-time algorithm to complete matrices of n rows and m columns to represent PPH solutions: we show that solving the problem requires recognizing special posets of width 2.     

一种在线推理模糊控制器算法的设计与实现

通过VC＋＋语言实现在线推理模糊控制器的算法,并以微机过程控制系统为研究对象,将液位传感器采集的信号通过PCI-9111HR系列数据采集卡送于在线推理式模糊控制器,实现对上、下水箱液位的控制,并把这种在线推理式模糊控制和人工控制的结果用webaeeess网际组态软件进行实时趋势曲线比较。实际结果验证了其算法的可行性。     

安全数据库的推理控制

首先对按元素划分安全级的多级数据库上由函数依赖(FD)和多值函数依赖(MVD)引起的推理问题进行了研究,所提出的推理控制算法在很大程度上提高了数据的可用性.为进一步有效防范推理所导致的敏感信息泄露,给出了基于视图的推理控制方法.该方法能够处理多视图合谋带来的安全问题.最后给出了视图依赖基划分原理,它是以后有关视图推理控制的基础.     

Genetic tuning of fuzzy inference within fuzzy classifier systems

Abstract: In generating a suitable fuzzy classifier system, significant effort is often placed on the determination and the fine tuning of the fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within the fuzzy rules. Often traditional fuzzy inference strategies are used which consequently provide no control over how strongly or weakly the inference is applied within these rules. Furthermore such strategies will allow no interaction between grades of membership. A number of theoretical fuzzy inference operators have been proposed for both regression and classification problems but they have not been investigated in the context of real-world applications. In this paper we propose a novel genetic algorithm framework for optimizing the strength of fuzzy inference operators concurrently with the tuning of membership functions for a given fuzzy classifier system. Each fuzzy system is generated using two well-established decision tree algorithms: C4.5 and CHAID. This will enable both classification and regression problems to be addressed within the framework. Each solution generated by the genetic algorithm will produce a set of fuzzy membership functions and also determine how strongly the inference will be applied within each fuzzy rule. We investigate several theoretical proven fuzzy inference techniques (T-norms) in the context of both classification and regression problems. The methodology proposed is applied to a number of real-world data sets in order to determine the effects of the simultaneous tuning of membership functions and inference parameters on the accuracy and robustness of fuzzy classifiers.     

Wizard: a database inference analysis and detection system

The database inference problem is a well-known problem in database security and information system security in general. In order to prevent an adversary from inferring classified information from combinations of unclassified information, a database inference analyst must be able to detect and prevent possible inferences. Detecting database inference problems at database design time provides great power in reducing problems over the lifetime of a database. We have developed and constructed a system called Wizard to analyze databases for their inference problems. The system takes as input a database schema, its constituent instances (if available) and additional human-supplied domain information, and provides a set of associations between entities and/or activities that can be grouped by their potential severity of inference vulnerability. A knowledge acquisition process called microanalysis permits semantic knowledge of a database to be incorporated into the analysis using conceptual graphs. These graphs are then analyzed with respect to inference-relevant domains we call facets using tools we have developed. We can determine inference problems within single facets as well as some inference problems between two or more facets. The architecture of the system is meant to be general so that further refinements of inference information subdomains can be easily incorporated into the system