Optimal Mining on Security Labels in Multilevel Security System
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    Abstract:

    This paper presents a bottom-up approach to implement the automatic and scientific transform of access control policies in the migration. First, the problem of mining security labels optimally is described formally, and it is then proved to be NP-complete. Next, an approximate optimization algorithm based on hierarchical clustering and genetic algorithm is presented, which decomposes the problem into two parts: category partition and secret level assignation. Finally, experimental results show that the algorithm is effective in finding an optimal solution. The proposed approach can be applied to migration projects in hierarchy protection in information security.

    Reference
    [1] Bell D, LaPadual LJ. Secure computer system: Unified exposition and MULTICS interpretation. Technical Report, MTR-2997Rev.1, Bedford: The MITRE Corporation, 1976.
    [2] Bell D, LaPadual LJ. Secure computer systems: Mathematical foundations. Technical Report, MTR-2547 Vol I, Bedford: TheMITRE Corporation, 1973.
    [3] Wu YJ, Liang HL, Zhao C. A multi-level security model with least privilege support for trusted subject. Journal of Software,2007,18(3):1-2 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/18/730.htm [doi: 10.1360/jos180730]
    [4] Li YF, Shen CX. A new security model for operating system. Science in China (Series E): Information Sciences, 2006,36(4):1-2 (in Chinese with English abstract).
    [5] Chaudhuri A, Naldurg P, Rajamani SK. EON: Modeling and analyzing dynamic access control systems with logic programs. In:Proc. of the 15th ACM Conf. on Computer and Communications Security (CCS 2008). New York: ACM Press, 2008. 1-2.
    [6] Vaidya J, Atluri V, Warner J. Roleminer: Mining roles using subset enumeration. In: Proc. of the 13th ACM Conf. on Computerand Communications Security (CCS 2006). New York: ACM Press, 2006. 1-2. [doi: 10.1145/1180405.1180424]
    [7] Zhang D, Ramamohanrao K, Ebringer T. Role engineering using graph optimisation. In: Proc. of the 12th ACM Symp. on AccessControl Models and Technologies (SACMAT 2007). New York: ACM Press, 2007. 1-2. [doi: 10.1145/1266840.1266862]
    [8] Vaidya J, Atluri V, Guo Q. The role mining problem: Finding a minimal descriptive set of roles. In: Proc. of the 12th ACM Symp.on Access Control Models and Technologies. New York: ACM Press, 2007. 1-2. [doi: 10.1145/1266840.1266870]
    [9] Lu H, Vaidya J, Atluri V. Optimal Boolean matrix decomposition: Application to role engineering. In: Proc. of the 24th Int’l Conf.on Data Engineering (ICDE 2008). Cancun: IEEE Computer Society Press, 2008. 1-2. [doi: 10.1109/ICDE.2008.4497438]
    [10] Frank M, Basin D, Buhmann JM. A class of probabilistic models for role engineering. In: Proc. of the 15th ACM Conf. onComputer and Communications Security (CCS 2008). New York: ACM Press, 2008. 1-2. [doi: 10.1145/1455770.1455809]
    [11] Molloy I, Chen H, Li TC, Calo S, Lobo J, Wang QH, Li NH, Bertino E. Mining roles with semantic meanings. In: Proc. of the 13thACM Symp. on Access Control Models and Technologies (SACMAT 2008). New York: ACM Press, 2008. 1-2. [doi:10.1145/1377836.1377840]
    [12] Bauer L, Garriss S, Reiter MK. Detecting and resolving policy misconfigurations in access-control systems. In: Proc. of the 13thACM Symp. on Access Control Models and Technologies. New York: ACM Press, 2008. 1-2. [doi: 10.1145/1377836.1377866]
    [13] Miettinen P. The discrete basis problem [MS. Thesis]. Helsinki: University of Helsinki, 2006.
    [14] Miettinen P, Mielikainen T, Gionis A, Das G, Mannila H. The discrete basis problem. IEEE Trans. on Knowledge and DataEngineering, 2008,20(10):1-2. [doi: 10.1109/TKDE.2008.53]
    [15] Berkhin P. Survey of clustering data mining techniques. Technical Report, EE242, San Jose: Accrue Software, 2002.
    [16] Xie X, Bcni G. A validity measure for fuzzy clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1991,13(8):84l-847. [doi: 10.1109/34.85677]
    [17] Kapp AV, Tibshirani R. Are clusters found in one datasetpresent in another dataset? Biostatistics, 2007,8(1):1-2.
    [18] Maekawa K, Mori N, Tamaki H, Kita H, Nishikawa Y. A genetic solution for the traveling salesman problem by means of athermodynamical selection rule. In: Fukuda T, Furuhashi T, eds. Proc. of the IEEE Conf. on Evolutionary Computation. New York:IEEE Press, 1996. 1-2. [doi: 10.1109/ICEC.1996.542655]
    [19] Rudolph G. Convergence analysis of canonical genetic algorithms. IEEE Trans. on Neural Networks, 1994,5(1):1-2. [doi:10.1109/72.265964]
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杨智,金舒原,段洣毅,方滨兴.多级安全中敏感标记的最优化挖掘.软件学报,2011,22(5):1020-1030

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History
  • Received:August 18,2009
  • Revised:February 01,2010
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