Ensemble of Soft Computing Techniques for Intrusion Detection

1
Deepika Veerwal
Deepika Veerwal
2
Naveen Choudhary
Naveen Choudhary
3
Dharm Singh
Dharm Singh
1 MPUAT

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In the present world scenario network-based computer systems have started to play progressively more vital roles. As a result they have become the main targets of our adversaries. To apply high security against intrusions and attacks, a number of software tools are being currently developed. To solve the problem of intrusion detection a number of pattern recognition and machine learning algorithms has been proposed. The paper states the problem of classifier fusion with soft labels for Intrusion Detection. Performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) is presented here. The performance of fusing these classifiers using approaches based on Dempster-Shafer Theory, Average Bayes Combination and Neural Network is proposed. As shown through the experimental results combined classifiers perform better than the individual classifiers.

14 Cites in Articles

References

  1. D Anderson,T Lunt,H Javitz,A Tamaru,A Valdes (1995). Detecting unusual program behavior using the statistical component of the nextgeneration intrusion detection expert system (nides).
  2. J Cannady (1998). Artificial neural networks for misuse detection.
  3. L Cordella,R Limongiello,C Sansone (2004). Network intrusion detection by a multi stage classification system.
  4. H Debar,M Becker,D Siboni (1992). A neural network component for an intrusion detection system.
  5. Hervé Debar,Marc Dacier,Andreas Wespi (2000). A revised taxonomy for intrusion-detection systems.
  6. D Denning (1987). An Intrusion-Detection Model.
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  8. G Giacinto,F Roli,L Didaci (2003). Fusion of multiple classifiers for intrusion detection in computer networks.
  9. G Giacinto,R Perdisci,M Delrio,F Roli (2008). Intrusion detection in computer networks by a modular ensemble of one-class classifiers.
  10. Wenke Lee,Salvatore Stolfo (1998). Data Mining Approaches for Intrusion Detection.
  11. Wenke Lee,Salvatore Stolfo,Kui Mok (1998). Mining in a data-flow environment.
  12. R Lippmann (2000). Improving intrusion detection performance using keyword selection and neural networks.
  13. S Mukkamala,A Sung,A Abraham (2005). Intrusion detection using ensemble of soft computing paradigms.
  14. G Rogova,R Menon (1998). Learning in distributed systems for decision making.

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.

Deepika Veerwal. 1970. \u201cEnsemble of Soft Computing Techniques for Intrusion Detection\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 13 (GJCST Volume 13 Issue E13): .

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GJCST Volume 13 Issue E13
Pg. 43- 47
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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In the present world scenario network-based computer systems have started to play progressively more vital roles. As a result they have become the main targets of our adversaries. To apply high security against intrusions and attacks, a number of software tools are being currently developed. To solve the problem of intrusion detection a number of pattern recognition and machine learning algorithms has been proposed. The paper states the problem of classifier fusion with soft labels for Intrusion Detection. Performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) is presented here. The performance of fusing these classifiers using approaches based on Dempster-Shafer Theory, Average Bayes Combination and Neural Network is proposed. As shown through the experimental results combined classifiers perform better than the individual classifiers.

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Ensemble of Soft Computing Techniques for Intrusion Detection

Deepika Veerwal
Deepika Veerwal MPUAT
Naveen Choudhary
Naveen Choudhary
Dharm Singh
Dharm Singh

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