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

Abstract

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.