基于粗糙集知识约简算法的行为审计研究

项目名称： 基于粗糙集知识约简算法的行为审计研究

项目编号： No.U1230117

项目类型： 联合基金项目

立项/批准年度： 2013

项目学科： 物理学II

项目作者： 李天瑞

作者单位： 西南交通大学

项目金额： 50万元

中文摘要： 日益增长的海量综合审计信息处理及其行为分析是当前一个热点研究领域，其核心内容之一是如何有效地监测异常行为。粗糙集理论是处理不精确、不完整和不一致等数据的重要工具，已成功应用到知识发现等领域。本项目基于粗糙集知识约简算法来研究行为审计，以多种信息系统和安全系统产生的审计相关的海量信息为研究对象，研究内容分为三个部分：1.研究海量综合审计信息中异常行为的知识发现方法和获取模型；2.基于粗糙集理论约简方法研究海量综合审计信息可变精度的数据缩减方法及关联规则生成算法，并设计模型与算法来评估数据缩减效率和失真性问题；3.研究提高异常行为检测的可靠性和准确性的方法及其改进技术。这些问题的解决，不仅可为复杂的行为审计提供理论、方法和算法，而且对于有效地监测海量综合审计信息中异常行为，增强系统的安全整体防范和预警能力，充分体现粗糙集在不确定性问题处理和知识发现中的优势以及拓展粗糙集的应用领域等有重要意义。

中文关键词： 行为审计；知识发现；粗糙集；约简；

英文摘要： The processing and behavior analysis of increasing massive and comprehensive audit data has been a hot research area. One of its key content is how to effectively monitor abnormal behaviors. Rough set theory is an important tool to deal with imprecise, incomplete and inconsistent data. It has been successfully applied to the field of knowledge discovery. This project is to study the behaviors of audit based on reduction algorithms in rough sets. The audit-related mass data from a variety of information systems and security systems is taken as the research object. The main work is divided into the following three parts: 1. Investigation of approaches and models for discovering abnormal behaviors in massive and comprehensive audit data; 2. Research on data reduction methods with variable accuracy for massive and comprehensive audit data, development of algorithms of generating association rules based on reduction methods in rough set theory and exploration of models and algorithms to evaluate the efficiency of data reduction and distortion; 3. Development of approaches and their optimization to improve the reliability and accuracy of the abnormal behavior detection. Solutions to these problems not only provide theory, methods and algorithms for behavior audit, but also contribute to effectively monitor abnormal be

英文关键词： Behavior audit；Knowledge discovery；Rough sets；Reduction；