Optimized Anomaly based Risk Reduction using PCA based Genetic Classifier
Keywords:
anomaly detection, PCA, genetic algorithm
Abstract
Security risk analysis is the thrust area for the information based world The researchers in this field deployed numerous techniques to overcome the information security oriented problem In this paper the researcher tried for a approach of using anomaly detection for the risk reduction The hub initiative for this work is that the anomalies are the deviation which could increase the percentage of risk The anomaly detection is guided by the PCA and the genetic based multi class classifier is used The classification is induced by the decision tree approach were the genetic algorithm is set out for the optimization in the process of finding the nodes of the tree The proposed approach is evaluated with the bench mark on PCA based ANN classifier The proposed approach outperforms the existing one The results are demonstrated
Downloads
- Article PDF
- TEI XML Kaleidoscope (download in zip)* (Beta by AI)
- Lens* NISO JATS XML (Beta by AI)
- HTML Kaleidoscope* (Beta by AI)
- DBK XML Kaleidoscope (download in zip)* (Beta by AI)
- LaTeX pdf Kaleidoscope* (Beta by AI)
- EPUB Kaleidoscope* (Beta by AI)
- MD Kaleidoscope* (Beta by AI)
- FO Kaleidoscope* (Beta by AI)
- BIB Kaleidoscope* (Beta by AI)
- LaTeX Kaleidoscope* (Beta by AI)
How to Cite
Published
2014-05-15
Issue
Section
License
Copyright (c) 2014 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.