An Attribute-weighted Clustering Intrusion Detection Method

Intrusion detection system is automatic system which recognize intrusions of computers or computer network systems. The existing security detection systems have many problems such as wrong detection of intrusions, false intrusions, poor real-time performance. To solve these problems, this paper improves the particle swarm optimization algorithm and presents an attribute-weighted distance calculation method based on information gain ratio. This method for the division of spherical or ellipsoidal data can obtain better clustering results. And the data set of KDD-cup 99 is used as the experimental data. The experimental results show that the method can detect many kinds of known network intrusion and also can detect many unknown network intrusions. At the same time, the method can maintain the higher intrusion detection rate and lower false alarm rate.

[1]  Yupin Luo,et al.  Saliency Detection by Selective Strategy for Salient Object Segmentation , 2012, J. Multim..

[2]  Bai Liang,et al.  K-Modes Clustering Algorithm Based on a New Distance Measure , 2010 .

[3]  Chen Li.  Adaptive Algorithm for Soft Subspace Clustering , 2010 .

[4]  Salvatore J. Stolfo,et al.  Mining Audit Data to Build Intrusion Detection Models , 1998, KDD.

[5]  Xiang Lian,et al.  General Cost Models for Evaluating Dimensionality Reduction in High-Dimensional Spaces , 2009, IEEE Transactions on Knowledge and Data Engineering.

[6]  Qing Wang,et al.  Tour Route Recommendation Begins with Multimodal Classification , 2012, J. Multim..

[7]  Swagatam Das,et al.  Automatic Clustering Using an Improved Differential Evolution Algorithm , 2007 .

[8]  Zhang Zhong-zhi.  Intrusion Detection Algorithm Based on Simulated Annealing and K-mean Clustering , 2010 .

[9]  Jianhua Lu,et al.  Call Admission Control and Scheduling Schemes with QoS Support for Real-time Video Applications in IEEE 802.16 Networks , 2006, J. Multim..

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

[11]  Michael Schatz,et al.  A Real-Time Intrusion Detection System Based on Learning Program Behavior , 2000, Recent Advances in Intrusion Detection.

[12]  Genyun Sun,et al.  A New Method for Edge Detection Based on the Criterion of Separability , 2011, J. Multim..

[13]  Jiuchao Feng,et al.  A sparse projection clustering algorithm , 2009 .

[14]  Ping Wang,et al.  Feature Particles Tracking for Moving Objects , 2012, J. Multim..

[15]  Ruimin Hu,et al.  An Even Grid Based Lattice Vector Quantization Algorithm for Mobile Audio Coding , 2011, J. Multim..

[16]  Sanghamitra Bandyopadhyay,et al.  A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters , 2008, IEEE Transactions on Knowledge and Data Engineering.

[17]  Eleazar Eskin,et al.  A GEOMETRIC FRAMEWORK FOR UNSUPERVISED ANOMALY DETECTION: DETECTING INTRUSIONS IN UNLABELED DATA , 2002 .