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网络攻击特征库的优化设计与实现 被引量:1

Optimal Design and Implementation on Feature Library of Network Attack
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摘要 随着网络入侵检测系统的广泛使用,作为系统核心部件的网络攻击特征库对网络入侵检测系统性能的影响越来越大。论文根据网络攻击特征库的特点对其进行了优化设计,将网络攻击特征库分解为入侵行为特征描述库和入侵确认库两个核心库,通过实验证明该设计方案可显著提高网络入侵检测系统的性能。 With the wide use of Network-based Intrusion Detection System(NIDS), the Network Intrusion Feature Library, as the basic and core component of NIDS has more and more influence, on the whole performance of NIDS. According to the characteristics of the Network Intrusion Feature Library, the paper describes the optimal design of the Feature Library in which the feature library is decomposed into two core components, that is, intrusion behavior char- acteristics description library and intrusion confirmation library. The experiment results show that this design scheme is useful and available, and can improve the whole performance of IDS.
出处 《信息安全与通信保密》 2009年第1期81-83,88,共4页 Information Security and Communications Privacy
基金 北京电子科技学院信息安全与保密重点实验室开放基金资助项目 中关村开放实验室专项项目 北京市委办局专项基金。
关键词 入侵检测系统 特征库 特征分类器 Intrusion Detection System(IDS) Feature Library Feature Classifier
作者简介 张宏宇。1977年生,硕士研究生,研究方向:网络信息安全; 刘宝旭,男,博士,副研究员,研究方向:网络信息安全。
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  • 1温世强,段海新,吴建平.开放式网络攻击特征库的设计与实现[J].小型微型计算机系统,2006,27(1):22-25. 被引量:5
  • 2胡威,李建华,陈波.入侵检测建模过程中特征提取最优化评估[J].计算机工程,2006,32(12):150-151. 被引量:2
  • 3R Heady, G Luger, A Maccabe et al. The architecture of a network level intrusion detection system [R]. Technical Report CS90-20, University of New Mexico, Department of Computer Science, August 1990.
  • 4Denning D E. An intrusion detection model[J]. IEEE Transactions on Software Engineering, February 1987 ,SE-13,222-232.
  • 5Common vulnerabilities and exposures, version 20030402[EB/OL]. http://cve. mitre. org, March 2004.
  • 6Northcutt S, Cooper M, Fearnow Met al. Intrusion signatures and analysis[M]. US: New Riders, 2001.
  • 7Security focus vulnerability database [EB/OL]. http ://www.securityfocus. com/bid, March 2004.
  • 8Arachnids [EB/OL]. http://www. whitehats.com/ids/index.html, March 2004.
  • 9CERT(r) incident note IN-2004-01[EB/OL]. http://www.cert. org/incident-notes/IN-2004-01. html, March 2004.
  • 10Snort. The open source network intrusion detection system[EB/OL]. http ://www. snort. org. March 2004.

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  • 1LEE W, STOLFO S J, MOK K W. Mining Audit Data to Build Intrusion Detection Models[C]//AAAI. Proc. of the 4th International Conference on Knowledge and Data Mining. New York: AAAIPress, 1998: 66-72.
  • 2AGRAWAL R, IMIELI?SKI T, SWAMI A. Mining Association Rules Between Sets of Items in Large Databases[C]//ACM. Proc. of the ACM S1GMOD Conference on Management of Data. New York: ACM Press, 1993: 207-216.
  • 3TAJBAKHSH A, RAHMATI M, MIZAEI A. Intrusion Detection Using Fuzzy Association Rules[J]. Applied Soft Computing, 2009, 09: 462-469.
  • 4HAN J, PEI J, YIN Y, et al. Mining Frequent Patterns Without Candidate Generation: A frequent-pattern Tree Approach[J]. Data Mining and Knowledge Discovery, 2004, 08(01): 53-87.
  • 5BEZDEK J C, ENRLICH R, FULL W. FCM: The Fuzzy C-means Clustering Algorithm[J]. Computers and Geosciences, 1984, 10(02): 191-203.
  • 6王红,张阳,李绪成.入侵检测中聚类应用的研究进展[J].信息安全与通信保密,2007,29(12):107-110. 被引量:2

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