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Exploratory Data Model for Effective WLAN Anomaly Detection based on Feature Construction and Reduction

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 6
Year of Publication: 2012
Ajay M. Patel
A. R. Patel
Hiral R. Patel

Ajay M Patel, A R Patel and Hiral R Patel. Article: Exploratory Data Model for Effective WLAN Anomaly Detection based on Feature Construction and Reduction. International Journal of Computer Applications 50(6):22-26, July 2012. Full text available. BibTeX

	author = {Ajay M. Patel and A. R. Patel and Hiral R. Patel},
	title = {Article: Exploratory Data Model for Effective WLAN Anomaly Detection based on Feature Construction and Reduction},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {6},
	pages = {22-26},
	month = {July},
	note = {Full text available}


The efficient and effective Anomaly detection system essentially requires identifying the behavior analysis for each activity. For this purpose unsupervised techniques are used but the accuracy and reliability of them results depend on the data set which have used for modeling. It is essential to identify important input features, missing values, redundancy, feature exploration etc… So for the data preprocessing different statistical analytical methods are used. In this paper, a statistical feature construction scheme is proposed based on Factor analysis. The proposed Feature construction model provides the way to remove redundancy, identify missing values and co-linearity between the initial data set. Experimental result shows the related good features are factorized using statistical measures. So it will improve the performance of the unsupervised algorithm results for the effective anomaly detection system.


  • Matrix Factorization Approach for Feature Deduction and Design of Intrusion Detection Systems. V´aclav Sn´a?sel, Jan Plato?s, Pavel Kr¨omer,Ajith Abraham.
  • Feature Construction Scheme for Efficient Intrusion Detection System. EUNHYE KIM, SEUNGMIN LEE, KIHOON KWON AND SEHUN KIM. 2010. Korea : JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 26, , 2010. 527-547 .
  • Factor-analysis based anomaly detection and clustering. Ningning Wua, *, Jing Zhangb. Elsevier. s. l. : Decision Support Systems 42 (2006), Elsevier, Vol. 42. 375– 389.
  • Data Preprocessing for anomaly based network intrusion detection: A Review. Jonathan J. Davis, Andrew J. Clark. 2011. s. l. : Computers & Securities, 2011, Vol. 30. 353-375.
  • DeCoster, Jamie. August 1, 1998. Overview of Factor Analysis. Department of Psychology, Tuscaloosa, AL 35487-0348 : August 1, 1998.
  • Anglim, Jeromy. 2007. Cluster Analysis & Factor Analysis. http://jeromyanglim. googlepages. com/. [Online] 2007. 325-711 Research Methods.
  • Field, Dr Andy. 2005. Factor Analysis using SPSS. s. l. : (Research Methods II): Factor Analysis on SPSS, 2005. C8057.