Feature Analysis for Intrusion Detection in Mobile Ad-hoc Networks

As Mobile Ad-hoc network (MANET) has become a very important technology, research concerning its security problem, especially, in intrusion detection has attracted many researchers. Feature selection methodology plays a central role in the data analysis process. The proposed features are tested in difierent network operating conditions. PCA is used to analyze the selected features. This is because, redundant and irrelevant features often reduce performance of the detection system. Performance reduction will occur both in speed and predictive accuracy. This paper aims to select and analyze the network features using principal component analysis. In this paper, performing various experiments, normal and attack states are simulated and the results for the selected features are analyzed.

[1]  Wenke Lee,et al.  A cooperative intrusion detection system for ad hoc networks , 2003, SASN '03.

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

[3]  Marco Richeldi,et al.  ADHOC: a tool for performing effective feature selection , 1996, Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence.

[4]  Yih-Chun Hu,et al.  Rushing attacks and defense in wireless ad hoc network routing protocols , 2003, WiSe '03.

[5]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[6]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[7]  Xia Wang,et al.  Feature Selection in Intrusion Detection System over Mobile Ad-hoc Network , 2005 .

[8]  R. Sekar,et al.  Specification-based anomaly detection: a new approach for detecting network intrusions , 2002, CCS '02.

[9]  Srikanth V. Krishnamurthy,et al.  Ad Hoc Networks: Technologies And Protocols , 2009, ADHOCNETS 2010.

[10]  Juha Karhunen,et al.  Generalizations of principal component analysis, optimization problems, and neural networks , 1995, Neural Networks.

[11]  Saswati Sarkar,et al.  Signature based intrusion detection for wireless ad-hoc networks: a comparative study of various routing protocols , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[12]  T.F. Lunt,et al.  Real-time intrusion detection , 1989, Digest of Papers. COMPCON Spring 89. Thirty-Fourth IEEE Computer Society International Conference: Intellectual Leverage.

[13]  Elizabeth M. Belding-Royer,et al.  A review of current routing protocols for ad hoc mobile wireless networks , 1999, IEEE Wirel. Commun..

[14]  Yasser Kamal Hassan,et al.  Performance Evaluation of Mobility Speed over MANET Routing Protocols , 2010, Int. J. Netw. Secur..

[15]  M.Z. Rahman,et al.  Learning intrusion detection based on adaptive bayesian algorithm , 2008, 2008 11th International Conference on Computer and Information Technology.

[16]  Yih-Chun Hu,et al.  Wormhole attacks in wireless networks , 2006, IEEE Journal on Selected Areas in Communications.

[17]  Nong Ye,et al.  Statistical process control for computer intrusion detection , 2001, Proceedings DARPA Information Survivability Conference and Exposition II. DISCEX'01.

[18]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[19]  Dharma P. Agrawal,et al.  SVM-based intrusion detection system for wireless ad hoc networks , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[20]  Afrand Agah,et al.  Preventing DoS Attacks in Wireless Sensor Networks: A Repeated Game Theory Approach , 2007, Int. J. Netw. Secur..

[21]  Qiang Yang,et al.  Document Transformation for Multi-label Feature Selection in Text Categorization , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[22]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[23]  E. Oja,et al.  Independent Component Analysis , 2013 .

[24]  Nei Kato,et al.  Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method , 2007, Int. J. Netw. Secur..

[25]  Wenke Lee,et al.  Intrusion detection in wireless ad-hoc networks , 2000, MobiCom '00.

[26]  Paul Gray,et al.  Introduction to Data Mining and Knowledge Discovery , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[27]  Byung-Joo Kim,et al.  Kernel based intrusion detection system , 2005, Fourth Annual ACIS International Conference on Computer and Information Science (ICIS'05).