International Journal of Emerging Trends & Technology in Computer Science
A Motivation for Recent Innovation & Research
ISSN 2278-6856
www.ijettcs.org

Call for Paper, Published Articles, Indexing Infromation EFFICIENT INTRUSION DETECTION SYSTEM WITH REDUCED DIMENSIONALITY, Authors : Nupur N. Majethiya , Prof. Dipak C. Patel, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), www.ijettcs.org
Volume & Issue no: Volume 4, Issue 2, March - April 2015

Title:
EFFICIENT INTRUSION DETECTION SYSTEM WITH REDUCED DIMENSIONALITY
Author Name:
Nupur N. Majethiya , Prof. Dipak C. Patel
Abstract:
Abstract Feature selection is an indispensable pre-processing step when mining huge datasets that can significantly improve the overall system performance. The filter phase select the features with highest information gain and this reduced feature subset is then passed to Kmeans clustering to identify normal and attack classes. This paper describes about a method of intrusion detection that uses machine learning algorithms. Here we discuss about the combinational use of two machine learning algorithms called Information Gain based Feature Selection and Kmeans Clustering Algorithm. Dimensionality Reduction is a field in machine learning that consist of mapping high dimensional data into low dimension while preserving important features of original dataset.The experiments were conducted on the intrusion detection dataset called NSL-KDD dataset. NSL-KDD intrusion detection dataset which is an enhanced version of KDDCUP 99 dataset.The comparison of the results with and without dimensionality reduction is also done. Keywords: Dimensionality Reduction, Information Gain based Feature Selection, KDDCUP’99 dataset, Kmeans Clustering.
Cite this article:
Nupur N. Majethiya , Prof. Dipak C. Patel , " EFFICIENT INTRUSION DETECTION SYSTEM WITH REDUCED DIMENSIONALITY " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 4, Issue 2, March - April 2015 , pp. 133-136 , ISSN 2278-6856.
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