论文信息 - An Algorithm for Automatic Clustering Number Determination in Networks Intrusion Detection

An Algorithm for Automatic Clustering Number Determination in Networks Intrusion Detection

To address the issue in fuzzy C-means algorithm (FCM) that clustering number has to be pre-defined,a clustering algorithm,F-CMSVM (fuzzy C-means and support vector machine algorithm),is proposed for automatic clustering number determination.Above all,the data set is classifed into two clusters by FCM.Then,support vector machine (SVM) with a fuzzy membership function is used to testify whether the data set can be classified further. Finally,the result of clusters can be obtained by repeating the computation process.Because affiliating matrix, obtained by the introduction of SVM into FCM,is defined to be the fuzzy membership function,each different input data sample can have different penalty value,and the separating hyper-plane is optimized.F-CMSVM is an unsupervised algorithm in which it is neither needed to label training data set nor specify clustering number.As shown from our simulation experiment over networks connection records from KDD CUP 1999 data set,F-CMSVM has efficient performance in clustering number optimization and intrusion detection.

Xiao Li

[1] Simin Nadjm-Tehrani,et al. ADWICE - Anomaly Detection with Real-Time Incremental Clustering , 2004, ICISC.

[2] Leonid Portnoy,et al. Intrusion detection with unlabeled data using clustering , 2000 .

[3] Hava T. Siegelmann,et al. Support Vector Clustering , 2002, J. Mach. Learn. Res..

[4] Salvatore J. Stolfo,et al. A data mining framework for building intrusion detection models , 1999, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344).

[5] Dae-Won Kim,et al. Validation of Fuzzy Partitions Obtained through Fuzzy C-Means Clustering , 2003, ISMIS.

[6] Li Kun,et al. Fuzzy Multi-Class Support Vector Machine and Application in Intrusion Detection , 2005 .

[7] Yang De. Research of the Network Intrusion Detection Based on Fuzzy Clustering , 2005 .

[8] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[9] Zhang Huan-guo. An Unsupervised Clustering-Based Intrusion Detection Method , 2003 .

[10] Jiang Zetao,et al. Unsupervised Texture Segmentation Based on FCM , 2005 .

[11] Lizhong Xiao,et al. K-means Algorithm Based on Particle Swarm Optimization Algorithm for Anomaly Intrusion Detection , 2006, 2006 6th World Congress on Intelligent Control and Automation.