基于聚类分析的客体聚合信息级别推演方法
doi: 10.3724/SP.J.1146.2011.01170
A Level Inference Method for Aggregated Information of Objects Based on Clustering Analysis
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摘要: 多级客体关系的复杂性,使得等级化网络存在着客体聚合引起信息泄露的问题。针对这一问题,该文提出了基于聚类分析的客体资源聚合信息级别的推演方法,首先依据属性重要程度,对客体属性进行约简,形成属性矢量;然后通过形式概念分析,计算概念引力,对同一安全域内的客体资源进行相似性分析,实现客体资源聚类;最后,依据属性或属性子集级别模糊集可能性测度,推演出由同类客体推导出更高级别信息的可能性。通过该方法,能够有效地制定等级化网络区域边界访问控制策略,控制主体对同一类客体的受限访问,从而降低信息系统失泄密的风险。Abstract: The relations among objects with secure level are very complex, which leads to the problems of security in multi-level network, such as information leakage by object aggregation. This paper puts forward a level inference method for aggregated information of objects based on clustering analysis. This method makes the reduction of attributes by the importance degree of attribute in one object, and attribute vector is formed. Then, according to formal concept analysis, this method accomplishes comparability analysis of objects in the same secure domain by gravity among concepts so that objects can be aggregated. Finally, according to probability estimate of fuzzy set about secure level of attributes or sets of attribute, probability of higher level information inferred by aggregation of similar objects is computed, which may effectively establish access control policy in multi-level network, and accomplish restricted access of congeneric objects in order to reduce the risk of information system.
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