Model Selection for Anomaly Detection in Wireless Ad Hoc Networks

Anomaly detection has been actively investigated to enhance the security of wireless ad hoc networks. However, it also presents a difficulty on model determination, such as feature selection and algorithm parameter optimization. In this paper, we address the issue of parameter selection for one-class support vector machine (1-SVM) based anomaly detection. We have investigated the performance of existing approaches, and also proposed a skewness-based outlier generation approach for parameter selection in the 1-SVM based anomaly detection model

[1]  M. Luban,et al.  An efficient method for generating a uniform distribution of points within a hyperspace , 1988 .

[2]  Robert P. W. Duin,et al.  Uniform Object Generation for Optimizing One-class Classifiers , 2002, J. Mach. Learn. Res..

[3]  Mary Baker,et al.  Mitigating routing misbehavior in mobile ad hoc networks , 2000, MobiCom '00.

[4]  Karl N. Levitt,et al.  A general cooperative intrusion detection architecture for MANETs , 2005, Third IEEE International Workshop on Information Assurance (IWIA'05).

[5]  Bernhard Schölkopf,et al.  SV Estimation of a Distribution's Support , 1999, NIPS 1999.

[6]  Kevin R. Fall,et al.  The NS Manual (Formerly NS Notes and Documentation , 2002 .

[7]  David M. J. Tax,et al.  One-class classification , 2001 .

[8]  H Deng,et al.  ROUTING SECURITY IN AD HOC NETWORKS , 2002 .

[9]  Thorsten Joachims,et al.  Estimating the Generalization Performance of an SVM Efficiently , 2000, ICML.

[10]  Xing Li,et al.  Evolving training model method for one-class SVM , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[11]  Chiman Kwan,et al.  Agent-based Distributed Intrusion Detection Methodology for MANETs , 2006, Security and Management.

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

[13]  Philip S. Yu,et al.  Cross-feature analysis for detecting ad-hoc routing anomalies , 2003, 23rd International Conference on Distributed Computing Systems, 2003. Proceedings..

[14]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[15]  Wenke Lee,et al.  Agent-based cooperative anomaly detection for wireless ad hoc networks , 2006, 12th International Conference on Parallel and Distributed Systems - (ICPADS'06).

[16]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.