Construction of Associative Algorithms of Frequent Invasion Sequence Based on Privacy-Processing

Hui Jie Zhu; Chun Bo Wei; Zhen Dong Gao

doi:10.4028/www.scientific.net/AMR.1079-1080.690

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1079-1080Construction of Associative Algorithms of Frequent...

Construction of Associative Algorithms of Frequent Invasion Sequence Based on Privacy-Processing

Abstract:

In order to work on research on analysis the relationship of invasive alarm, establish a net-safe frame integrated technology of privacy processing. The mining algorithms of K- Frequent Patterns is improved in research on quantity of invasive alarm, a generalization measure method has been proposed which focus on effectiveness ，by improving the algorithms, bring out associative algorithms of frequent invasion sequence with privacy-processing integrated, privacy-processing of invasive alarm data has been achieved effectively. Experiment shows that the association rules which get from improved algorithms are available, and the improved algorithms have the ability to protect the sensitive information. A conclusion has been made from the results: After privacy-processing, the frequent invasion sequence algorithms have preferable validity, scalability and mining performance.

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Periodical:

Advanced Materials Research (Volumes 1079-1080)

Pages:

690-693

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1079-1080.690

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Online since:

December 2014

Authors:

Hui Jie Zhu, Chun Bo Wei, Zhen Dong Gao

Keywords:

Data-Mining, Invasive Alarm, Network Security, Privacy-Processing

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References

[1] F. Cuppens, A. Miege. Alert correlation in a cooperative intrusion detection framework[C]. In: Proceedings of IEEE Symposium on Security and Privacy, 2002. 202-215.

DOI: 10.1109/secpri.2002.1004372

Google Scholar

[2] P. Ning, Y. Cui, D. S. Reeves. Constructing attack scenarios through correlation of intrusion alerts[C]. In: Proceedings of the 9th ACM Conference on Computer and Communications Security, 2002, 245-254.

DOI: 10.1145/586110.586144

Google Scholar

[3] J. Zhou, M. Heckman, B. Reynolds et al. Modeling network intrusion detection alerts for correlation[J]. ACM Transactions on Information and System Security (TISSEC)，2007, 10: 1-4.

DOI: 10.1145/1210263.1210267

Google Scholar

[4] R. Agrawal, R. Srikant. Fast algorithms for mining association rules[C]. In: Proc. 20th Int. Conf. Very Large Data Bases, VLDB, 1994: 487-499.

Google Scholar

[5] J. S. Park, M. S. Chen, P. S. Yu, An effective hash-based algorithm for mining association rules[J] . ACM, (1995).

Google Scholar

[6] A. Savasere, E. R. Omiecinski, S. B. Navathe. An efficient algorithm for mining association rules in large databases[J] . (1995).

Google Scholar