| 基于多层次优化技术的XACML 策略评估引擎 |
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| 引用本文： | 王雅哲,冯登国,张立武,张敏.基于多层次优化技术的XACML 策略评估引擎[J].软件学报,2011,22(2):323-338. |
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| 作者姓名： | 王雅哲 冯登国 张立武 张敏 |
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| 作者单位： | 1. 中国科学院,软件研究所,信息安全国家重点实验室,北京,100190;信息安全共性技术国家工程研究中心,北京,100190
2. 中国科学院,软件研究所,信息安全国家重点实验室,北京,100190 |
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| 基金项目： | 国家自然科学基金(61003228, 60803129); 中国科学院知识创新工程项目(YYYJ-1013); 下一代互联网业务试商用及设备产业化专项(CNGI-09-03-03) |
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| 摘 要： | 给出一种采用多层次优化技术的XACML(extensible access control markup language)策略评估引擎实现方案MLOBEE(multi-level optimization based evaluation engine).策略判定评估前,对原始策略库实施规则精化,缩减策略规模并调整规则顺序;判定评估过程中,在引擎内部采用多种缓存机制,分别建立判定结果缓存、属性缓存和策略缓存,有效降低判定引擎和其他功能部件的通信损耗.通过两阶段索引实现的策略缓存,可显著降低匹配运算量并提高策略匹配准确率.仿真实验验证了MLOBEE所采用的多层次优化技术的有效性,其整体评估性能明显优于大多数同类系统.
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| 关 键 词： | XACML 访问控制 策略评估 规则精化 缓存机制 策略索引 |
| 收稿时间： | 2009/2/20 0:00:00 |
| 修稿时间： | 2009/8/12 0:00:00 |
XACML Policy Evaluation Engine Based on Multi-Level Optimization Technology |
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| WANG Ya-Zhe,FENG Deng-Guo,ZHANG Li-Wu and ZHANG Min.XACML Policy Evaluation Engine Based on Multi-Level Optimization Technology[J].Journal of Software,2011,22(2):323-338. |
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| Authors: | WANG Ya-Zhe FENG Deng-Guo ZHANG Li-Wu and ZHANG Min |
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| Affiliation: | State Key Laboratory of Information Security, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China;National Engineering Research Center of Information Security, Beijing 100190, China;State Key Laboratory of Information Security, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China;National Engineering Research Center of Information Security, Beijing 100190, China;State Key Laboratory of Information Security, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China;State Key Laboratory of Information Security, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China |
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| Abstract: | This paper proposes an implementation scheme of XACML (extensible access control markup language) policy evaluation engine based on multi-level optimization technology, MLOBEE (multi-level optimization based evaluation engine). Before evaluating these policies, the scenario implements rule refinement to lessen scale policies and adjust the sequence at the rule. During evaluation, the engine adopts a multi-cache mechanism that includes result cache, attribute cache, and policy cache to reduce the communication cost between engine and other components. To decrease matching magnitudes and enhance matching exactitudes, policy cache practices two stage index techniques. Finally, emulation tests validate that the overall evaluation performance of MLOBEE, using multi-level optimization technology, is better than most other similar systems. |
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| Keywords: | XACML (extensible access control markup language) access control policy evaluation rule refining cache mechanism policy index |
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