Feature Extraction based Approaches for Improving the Performance of Intrusion Detection Systems

—In recent years, the rapid development of information and communication technology results in too many loopholes in the network, and thus attracts lots of hackers' attacks. Intrusion Detection System (IDS) has been developed to detect these attacks. Depending on different data and analysis methods, this system will have different detection methods. But, there is no one model is absolutely effective. Therefore, this study will focus on improving the classification performance of anomaly detection. In this study, we'll propose " Local Latent Semantic Indexing (LLSI) " and " Local Kernel-Principal Component Analysis (LKPCA) " based methods, which introduce class information to feature extraction techniques. And the proposed methods will be integrated into support vector machine (SVM) to improve the performance of classification. Finally, KDD-NSL data set will be employed to testify the effectiveness of the proposed methods.

[1]  Dhruba K. Bhattacharyya,et al.  Network Anomaly Detection: A Machine Learning Perspective , 2013 .

[2]  LeeSeungmin,et al.  A novel hybrid intrusion detection method integrating anomaly detection with misuse detection , 2014 .

[3]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  M. Govindarajan.  Intrusion Detection using an Ensemble of Classification Methods , 2012 .

[5]  Oscar Castillo,et al.  Proceedings of the International MultiConference of Engineers and Computer Scientists 2007, IMECS 2007, March 21-23, 2007, Hong Kong, China , 2007, IMECS.

[6]  Xiangji Huang,et al.  Mining network data for intrusion detection through combining SVMs with ant colony networks , 2014, Future Gener. Comput. Syst..

[7]  Wei-Ying Ma,et al.  Improving text classification using local latent semantic indexing , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[8]  Habiba Drias,et al.  An intrusion detection and alert correlation approach based on revising probabilistic classifiers using expert knowledge , 2012, Applied Intelligence.

[9]  Susan T. Dumais,et al.  Using Linear Algebra for Intelligent Information Retrieval , 1995, SIAM Rev..

[10]  Minyi Guo,et al.  Fast dimension reduction for document classification based on imprecise spectrum analysis , 2010, Inf. Sci..

[11]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[12]  Anju Vyas.  Print , 2003 .

[13]  Roberto Battiti,et al.  Identifying intrusions in computer networks with principal component analysis , 2006, First International Conference on Availability, Reliability and Security (ARES'06).

[14]  Haixia Xu,et al.  Adaptive Kernel Principal Analysis for Online Feature Extraction , 2009 .

[15]  Ning Wang,et al.  The optimization of the kind and parameters of kernel function in KPCA for process monitoring , 2012, Comput. Chem. Eng..

[16]  Siyang Zhang,et al.  A novel hybrid KPCA and SVM with GA model for intrusion detection , 2014, Appl. Soft Comput..

[17]  Wei Cong,et al.  Anomaly intrusion detection based on PLS feature extraction and core vector machine , 2013, Knowl. Based Syst..

[18]  Chou-Yuan Lee,et al.  An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection , 2012, Appl. Soft Comput..

[19]  Zhenguo Chen,et al.  Minimax Probability Machine Classifier with Feature Extraction by Kernel Pca for Intrusion Detection , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[20]  Jun Wang,et al.  A classification approach for less popular webpages based on latent semantic analysis and rough set model , 2015, Expert Syst. Appl..

[21]  Adel Sabry Eesa,et al.  A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems , 2015, Expert Syst. Appl..

[22]  Alkiviadis G. Akritas,et al.  Applications of singular-value decomposition (SVD) , 2004, Math. Comput. Simul..

[23]  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).

[24]  N. Balakrishnan,et al.  Performance enhancement of Intrusion Detection Systems using advances in sensor fusion , 2008, 2008 11th International Conference on Information Fusion.

[25]  Jugal K. Kalita,et al.  MIFS-ND: A mutual information-based feature selection method , 2014, Expert Syst. Appl..

[26]  Zhifang He,et al.  Stock Price Prediction based on SSA and SVM , 2014, ITQM.

[27]  Sung-Bae Cho,et al.  Rule-based integration of multiple measure-models for effective intrusion detection , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[28]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[29]  Alexander Thomasian,et al.  CSVD: Clustering and Singular Value Decomposition for Approximate Similarity Search in High-Dimensional Spaces , 2003, IEEE Trans. Knowl. Data Eng..

[30]  Chun-Chin Hsu,et al.  An information granulation based data mining approach for classifying imbalanced data , 2008, Inf. Sci..

[31]  Norrozila Sulaiman,et al.  Intrusion Detection System Based on SVM for WLAN , 2012 .

[32]  Lei Zhang,et al.  A multi-manifold discriminant analysis method for image feature extraction , 2011, Pattern Recognit..

[33]  Serkan Günal,et al.  A novel probabilistic feature selection method for text classification , 2012, Knowl. Based Syst..

[34]  Lan Wang,et al.  Face recognition based on PCA and logistic regression analysis , 2014 .

[35]  Neminath Hubballi,et al.  False alarm minimization techniques in signature-based intrusion detection systems: A survey , 2014, Comput. Commun..