A Cost Sensitive Machine Learning Approach for Intrusion Detection

A Cost Sensitive Machine Learning Approach for Intrusion Detection

Adamu Teshome

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Dr.Vuda Sreenivasa Rao

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Bahir Dar University

A Cost Sensitive Machine Learning Approach for Intrusion Detection

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Abstract

The problems with the current researches on intrusion detection using data mining approach are that they try to minimize the error rate (make the classification decision to minimize the probability of error) by totally ignoring the cost that could be incurred. However, for many problem domains, the requirement is not merely to predict the most probable class label, since different types of errors carry different costs. Instances of such problems include authentication, where the cost of allowing unauthorized access can be much greater than that of wrongly denying access to authorized individuals, and intrusion detection, where raising false alarms has a substantially lower cost than allowing an undetected intrusion. In such cases, it is preferable to make the classification decision that has minimum cost, rather than that with the lowest error rate.For this reason, we examine how cost-sensitive machine learning methods can be used in Intrusion Detection systems. The performance of the approach is evaluated under different experimental conditions and different models in comparison with the KDD Cup 99 winner resultsin terms of average misclassification cost, as well as detection accuracy and false positive ratesthough the winner used original KDD dataset whereas for this research NSL-KDD dataset which is new version of the original KDD cup data and it is better than the original dataset in that it has no redundant data is used.

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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

Adamu Teshome. 2014. \u201cA Cost Sensitive Machine Learning Approach for Intrusion Detection\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C6): .

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GJCST Volume 14 Issue C6
Pg. 1- 8

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

cost sens-itive learning Data mining Intrusion Detection

Submission ReceivedDecember 11, 2013
Peer Review Double Blind
Handling Editor
Accepted January 4, 2014
Published January 15, 2014

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September 6, 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: Adamu Teshome, Dr.Vuda Sreenivasa Rao (PhD/Dr. count: 1)

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