An Accurate Method for Deteetion of Cyber Attaeks
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Date
2013
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Abstract
Due to the rapid growth of wireless local area networks in day-to-day applications detection
of cyber attacks have been increasing. In 2012, a survey stated that an organization experiences with an
average of 102 attacks every week, whereas 72 attacks and 50 attacks were detected during 2011 and
2010, approxmiately 42% of attacks have increased in a year. In this paper, the dimensionality
reduction techniques are used to detect the cyber attacks. The objective of this research work is to
extract the meaningful class labels in the data set using linear dimensionality reduction techniques like
Principal Components Analysis (PCA), Independent Component Analysis (ICA) and Linear
Discriminant analysis (LDA). The experimental study shows that PCA performs better than LDA and
ICA. To improve the obtained results the PCA is combined with SVM, where an accuracy rate
increases in the detection of cyber attacks.