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Analyzing the Behavior of Multiple Dimensionality
In the ubiquitously connected world of IT infrastructure, Intrusion Detection System (IDS) plays vital role. IDS is
considered as a critical component of security infrastructure and is implemented either through hardware or software devices
and can detect malicious activities in a networked environment. To detect or prevent network attacks, Network Intrusion
Detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection
speed. Analyzing different attacks effectively through Dimensionality Reduction Algorithms is an efficient mechanism. The
significance of these algorithms is they improvise feature selection from huge datasets. Also through this the learning speed is
enhanced. Speed is a crucial parameter in the success of network intrusion detection systems for defending reactions. In this
paper open source datasets Knowledge Discovery in Databases (KDD CUP) dataset and 10% KDD CUP dataset are
employed for experimentation. These datasets are provided to Dimensionality Reduction Algorithms like Principal Component
Analysis (PCA), Linear Discriminate Analysis (LDA) and Kernel PCA with different kernels and classified with Logistic
Regression classification algorithm for procuring accurate results. Further to boost up the accuracy achieved so far K-fold
algorithm is utilized. Finally a comparative study of different accuracy results is done by using K-fold algorithm and also
without the usage of this algorithm. The empirical study on KDD CUP data confirms the effectiveness of the proposed scheme.
In this paper we discovered the combination of multiple dimensionality reduction algorithm such as PCA , LDA and Kernel
PCA with classification algorithm and this combination of algorithm gives best result. Our study will help out the researchers
to uncover critical area such as intrusion detection in network traffic environment. The results what we identified will be very
much helpful for researchers for their future research on KDD CUP dataset. In this the new theory will be arrived by this
research that the best accuracy achieved by PCA with 10% KDD CUP dataset experimental results without KFold attained
98% and with KFold attained 99%. LDA with 10% KDD CUP Dataset experimental results without KFold attained 98% and
with KFold attained 99%.