Improving Intrusion Detection with Adaptive Support Vector Machines

  • N. Macek School of Electrical Engineering and Computing of Applied Studies
  • B. Dordevic Institute Mihailo Pupin d.o.o
  • V. Timcenko Institute Mihailo Pupin d.o.o
  • M. Bojovic Omladinskih brigada
  • M. Milosavljevic Singidunum University
Keywords: Intrusion detection, machine learning, support vector machines, false negative rate

Abstract

The research topic that this paper is focused on is intrusion detection in critical network infrastructures, where discrimination of normal activity can be easily corrected, but no intrusions should remain undetected. The intrusion detection system presented in this paper is based on support vector machines that classify unknown data instances according both to the feature values and weight factors that represent importance of features towards the classification. The major contribution of the proposed model is significantly decreased false negative rate, even for the minor categories that have a very few instances in the training set, indicating that the proposed model is suitable for aforementioned environments.

DOI: http://dx.doi.org/10.5755/j01.eee.20.7.8025

Published
2014-08-29
How to Cite
Macek, N., Dordevic, B., Timcenko, V., Bojovic, M., & Milosavljevic, M. (2014). Improving Intrusion Detection with Adaptive Support Vector Machines. Elektronika Ir Elektrotechnika, 20(7), 57-60. https://doi.org/10.5755/j01.eee.20.7.8025
Section
SYSTEM ENGINEERING, COMPUTER TECHNOLOGY