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Information Technology Journal

Year: 2011 | Volume: 10 | Issue: 3 | Page No.: 648-655
DOI: 10.3923/itj.2011.648.655

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Authors


Z. Muda

Country: Malaysia

W. Yassin

Country: Malaysia

M. N. Sulaiman

Country: Malaysia

N. I. Udzir

Country: Malaysia

Keywords


  • accuracy
  • detection rate
  • Intrusion detection system
  • K-Means clustering
  • Na�ve Bayes classifier
  • false alarm
Research Article

A K-Means and Naive Bayes Learning Approach for Better Intrusion Detection

Z. Muda, W. Yassin, M. N. Sulaiman and N. I. Udzir
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Citation to this article as recorded by ASCI logo

Asif-Iqbal, H., N.I. Udzir, R. Mahmod and A.A.A. Ghani, 2011. Filtering events using clustering in heterogeneous security logs. Inform. Technol. J., 10: 798-806.
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Hashemi, V.M., Z. Muda and W. Yassin, 2013. Improving intrusion detection using genetic algorithm. Inform. Technol. J., 12: 2167-2173.
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Hsu, S.C., 2012. The RFM-based institutional customers clustering: case study of a digital content provider. Inform. Technol. J., 11: 1193-1201.
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Lei, X. and P. Zhou, 2012. An intrusion detection model based on GS-SVM Classifier. Inform. Technol. J., 11: 794-798.
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Liu, S.L, Y.H. Liu, Y.F. Tang and R.H. Jiang, 2012. A novel pattern recognition approach based on immunology. Inform. Technol. J., 11: 134-140.
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Rasheed, M.M., O. Ghazali and N.M. Norwawi, 2012. Intelligent signature detection for scanning internet worms. Inform. Technol. J., 11: 760-767.
CrossRef

Rasheed, M.M., O. Ghazali and R. Budiarto, 2012. Fast detection of stealth and slow scanning worms in transmission control protocol. J. Applied Sci., 12: 2156-2163.
CrossRefDirect Link

Citation to this article as recorded by Crossref logo

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How to cite this article

Z. Muda, W. Yassin, M. N. Sulaiman and N. I. Udzir, 2011. A K-Means and Naive Bayes Learning Approach for Better Intrusion Detection. Information Technology Journal, 10: 648-655.

DOI: 10.3923/itj.2011.648.655

URL: https://scialert.net/abstract/?doi=itj.2011.648.655

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