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Reseach Article

Anomaly Detection based on Machine Learning: Dimensionality Reduction using PCA and Classification using SVM

by Annie George
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 21
Year of Publication: 2012
Authors: Annie George
10.5120/7470-0475

Annie George . Anomaly Detection based on Machine Learning: Dimensionality Reduction using PCA and Classification using SVM. International Journal of Computer Applications. 47, 21 ( June 2012), 5-8. DOI=10.5120/7470-0475

@article{ 10.5120/7470-0475,
author = { Annie George },
title = { Anomaly Detection based on Machine Learning: Dimensionality Reduction using PCA and Classification using SVM },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 21 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number21/7470-0475/ },
doi = { 10.5120/7470-0475 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:42:25.863461+05:30
%A Annie George
%T Anomaly Detection based on Machine Learning: Dimensionality Reduction using PCA and Classification using SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 21
%P 5-8
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Anomaly detection has emerged as an important technique in many application areas mainly for network security. Anomaly detection based on machine learning algorithms considered as the classification problem on the network data has been presented here. Dimensionality reduction and classification algorithms are explored and evaluated using KDD99 dataset for network IDS. Principal Component Analysis for dimensionality reduction and Support Vector Machine for classification have been considered for the application on network data and the results are analysed. The result shows the decrease in execution time for the classification as we reduce the dimension of the input data and also the precision and recall parameter values of the classification algorithm shows that the SVM with PCA method is more accurate as the number of misclassification decreases.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection Anomaly Detection Principal Component Analysis Support Vector Machine