期刊文献+

基于混合AIS/SOM的入侵检测模型 被引量:2

Intrusion Detection Model Based on Hybrid AIS/SOM
下载PDF
导出
摘要 针对异常检测信息获取不足的缺点,提出基于混合人工免疫系统(AIS)/自组织映射(SOM)的入侵检测模型。该模型采用人工免疫系统检测网络异常,对检测到的异常连接用自组织映射进行分类,应用KDDCUP99实验数据集进行仿真。结果表明该检测方法是有效的,能够将检测到的异常连接分类并给出异常连接的更多信息,检测和分类效率较高、误报率低。 This paper proposes an intrusion detection model based on Artificial Immune System(AIS)/Self Organizing Map(SOM). It detects anomaly attack by AIS, and applies SOM to the detected-anomaly classification. This model can detect the unknown attaction and get more information about intrusion due to the combinition with the advantages of misuse detection and anomaly detection, and simulates it with KDDCUP 99 data. Experimental results show that the method is effective, which can classify the detected anomaly connections and give more information about such anomaly connection with low false rate and high positive rate.
出处 《计算机工程》 CAS CSCD 北大核心 2010年第12期164-166,共3页 Computer Engineering
关键词 人工免疫系统 自组织映射 入侵检测 遗传算法 异常检测 Artificial Immune System(AIS) Self Organizing Map(SOM) intrusion detection Genetic Algorithm(GA) anomaly detection
  • 相关文献

参考文献8

  • 1Denning D E.An Intrusion Detection Model[J].IEEE Transactions on Software Engineering,1987,13(2):222-232. 被引量:1
  • 2Shon T,Moon J.A Hybrid Machine Learning Approach to Network Anomaly Detection[J].Information Sciences,2007,177(18):3799-3821. 被引量:1
  • 3白琳.基于免疫遗传聚类的异常检测系统[J].西安邮电学院学报,2008,13(1):103-108. 被引量:2
  • 4Gonzalez F,Dasgupta D.An Immunity-based Technique to Characterize Intrusions in Computer Networks[J].IEEE Transactions on Evolutionary Computation,2002,6(3):281-291. 被引量:1
  • 5Gonzalez F,Dasgupta D.Anomaly Detection Using Real-valued Negative Selection[J].Genetic Programming and Evolvable Machines,2003,4(4):383-403. 被引量:1
  • 6罗敏,王丽娜,张焕国.基于无监督聚类的入侵检测方法[J].电子学报,2003,31(11):1713-1716. 被引量:64
  • 7HanJiawei MichelineKambe.数据挖掘概念与技术[M].北京:机械工业出版社,2001.. 被引量:149
  • 8Pfahringer B.Winning Entry of the KDDCUP99 Classifier Learning Contest[Z].[1999-10-23].http://www.acm.org/sigkdd/kddcup/. 被引量:1

二级参考文献10

共引文献211

同被引文献16

  • 1Maki Y, Loparo K A. Neural-network Approach to Fault Detectionand Diagnosis in Industrial Processes[J]. IEEE Trans. on ControlSystems Technology, 1997, 5(6): 529-541. 被引量:1
  • 2Kohonen T. Self-organizing Maps[M]. New York, USA: Springer Verlag, 1997. 被引量:1
  • 3Su M C, Chang H T. A New Model of Self-organizing NeuralNetworks and Its Application in Data Projection[J]. IEEE Trans.on Neural Networks, 2001, 12(1): 153-158. 被引量:1
  • 4Lirn Y W, Lee S U. On the Color Image Segmentation AlgorithmBased on the Thresholding and the Fuzzy C-means Technique[J].Pattern Recognition, 1999, 23(9): 935-952. 被引量:1
  • 5Khatib O. Real Time Obstacle Avoidance for Manipulators andMobile Robots[C]//Proc. of IEEE International Conference onRobotics and Automation. St. Louis, Missouri, USA: [s. n.], 1985:500-505. 被引量:1
  • 6WARRENDER C, FORREST S, PEARLMUTTER B. Detecting intrusions using system caIls: Alternative data models[C]//Proceedings of 1999 IEEE Symposium on Security and Privacy. Washington, DC: IEEE Computer Society, 1999:133 - 145. 被引量:1
  • 7MAO GUOJUN, WU XUDONG, CHEN GONG. Mining maximal frequent itemsets from data streams [ J]. Journal of Information Sci- ence, 2007, 33 (3) : 251 - 262. 被引量:1
  • 8TMITCHELL A B. Combining labeled and unlabeled data with cotraining[ C]// Proceedings of the l lth Annual Conference on Com- putational Learning Theory. New York: ACM, 1998:131 - 140. 被引量:1
  • 9KDD99[ EB/OL]. [2011 - 02 - 15]. http://kdd, ics. uci. edu/databases/kddcup99/task, html. 被引量:1
  • 10Libsvm[ EB/OL]. [2011 -02 -20]. http://www, csie. ntu. edu. tw/ - cjlin/libsvm/. 被引量:1

引证文献2

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部