A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

  • Saichon Jaiyen King Mongkut’s Institute of Technology Ladkrabang
  • Ployphan Sornsuwit King Mongkut’s Institute of Technology Ladkrabang
Keywords: cybersecurity, IDTL, incremental learning

Abstract

A cyber-attack detection is currently essential for computer network protection. The fundamentals of protection are to detect cyber-attack effectively with the ability to combat it in various ways and with constant data learning such as internet traffic. With these functions, each cyber-attack can be memorized and protected effectively any time. This research will present procedures for a cyber-attack detection system Incremental Decision Tree Learning (IDTL) that use the principle through Incremental Linear Discriminant Analysis (ILDA) together with Mahalanobis distance for classification of the hierarchical tree by reducing data features that enhance classification of a variety of malicious data. The proposed model can learn a new incoming datum without involving the previous learned data and discard this datum after being learned. The results of the experiments revealed that the proposed method can improve classification accuracy as compare with other methods. They showed the highest accuracy when compared to other methods. If comparing with the effectiveness of each class, it was found that the proposed method can classify both intrusion datasets and other datasets efficiently.

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Author Biographies

Saichon Jaiyen

Department of Computer Science, Faculty of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand

Ployphan Sornsuwit

Department of Computer Science, Faculty of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand

Published In
Vol 23 No 5, Sep 30, 2019
How to Cite
[1]
S. Jaiyen and P. Sornsuwit, “A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance”, Eng. J., vol. 23, no. 5, pp. 71-88, Sep. 2019.