Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection

Authors

  • Loka Raj Ghimire Department of graduate study, Nepal College of Information Technology, Nepal
  • Roshan Chitrakar

    Department of graduate study, Nepal College of Information Technology, Nepal

DOI:

https://doi.org/10.30564/jcsr.v3i2.2922

Abstract

Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity. Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability. Nonetheless, it is Naïve use of the mean data value for the cluster core that presents a major drawback. The chances of two circular clusters having different radius and centering at the same mean will occur. This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together. However, if the clusters are not spherical, it fails. To overcome this issue, a new integrated hybrid model by integrating expectation maximizing (EM) clustering using a Gaussian mixture model (GMM) and naïve Bays classifier have been proposed. In this model, GMM give more flexibility than K-Means in terms of cluster covariance. Also, they use probabilities function and soft clustering, that’s why they can have multiple cluster for a single data. In GMM, we can define the cluster form in GMM by two parameters: the mean and the standard deviation. This means that by using these two parameters, the cluster can take any kind of elliptical shape. EM-GMM will be used to cluster data based on data activity into the corresponding category.

Keywords:

Anomaly detection; Clustering; EM classification; Expectation maximization (EM); Gaussian mixture model (GMM); GMM classification; Intrusion detection; Naïve Bayes classification

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How to Cite

Ghimire, L. R., & Chitrakar, R. (2021). Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection. Journal of Computer Science Research, 3(2), 1–10. https://doi.org/10.30564/jcsr.v3i2.2922

Issue

Vol. 3 , Iss. 2 (April 2021)

Article Type

Article

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Copyright © 2021 Author(s)

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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