Optimized Anomaly based Risk Reduction using PCA based Genetic Classifier

Optimized Anomaly based Risk Reduction using PCA based Genetic Classifier

C.Kavitha

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Dr. K.Iyakutti

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α Madurai Kamaraj University

Optimized Anomaly based Risk Reduction using PCA based Genetic Classifier

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CSTSDEPC68H

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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.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

C.Kavitha. 2014. \u201cOptimized Anomaly based Risk Reduction using PCA based Genetic Classifier\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C7): .

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GJCST Volume 14 Issue C7
Pg. 31- 37

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

anomaly detection Genetic Algorithm PCA

Submission ReceivedDecember 8, 2013
Peer Review Double Blind
Handling Editor
Accepted January 3, 2014
Published January 15, 2014

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v1.2

Issue date

September 25, 2014

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: C.Kavitha, Dr. K.Iyakutti (PhD/Dr. count: 1)

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Total Views: 8696

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