Intelligent Intrusion Detection Based on Genetically Tuned Artificial Neural Networks

Leon Reznik; Michael J. Adams; Bryan Woodard

doi:10.20965/jaciii.2010.p0708

single-jc.php

« previous

JACIII Vol.14 No.6 pp. 708-713

doi: 10.20965/jaciii.2010.p0708

(2010)

Paper:

Views over last 60 days: 531

Intelligent Intrusion Detection Based on Genetically Tuned Artificial Neural Networks

Leon Reznik, Michael J. Adams, and Bryan Woodard

Department of Computer Science, Rochester Institute of Technology, 102 Lomb Memorial Drive, Rochester, NY 14623, USA

Received:

February 10, 2010

Accepted:

April 10, 2010

Published:

September 20, 2010

Keywords:

intrusion detection, artificial neural networks, genetic algorithms

Abstract

Artificial intelligence techniques and in particular neural networks (ANN) have been widely employed in an Intrusion Detection System (IDS) design. Due to their high learning ability, their application allows achieving higher performance in various applications. However, they can improve the resource consumption at the same time. This design goal, which is very important in a real life IDS design has received much less attention so far. The paper investigates the optimization methods of improving the ANN-based IDS performance along with the resource consumption. A particular consideration is given to partially connected neural networks that open another way of tuning the ANN structure toward the application. The study examines the choice of the connectivity ratio and its optimization with genetic algorithms. Various genetic algorithms parameters are tested in computer network attacks detection and recognition problems. The results are analyzed and IDS design recommendations are provided.

Cite this article as:

L. Reznik, M. Adams, and B. Woodard, “Intelligent Intrusion Detection Based on Genetically Tuned Artificial Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.6, pp. 708-713, 2010.

Data files:

References

[1] A. Canning and E. Gardner, “Partially Connected Models of Neural Networks,’ J. of Physics, A Mathematical General, Vol.21, pp. 3275-3284, August 1988.
[2] D. Elizondo and E. Fiesler, “A survey of partially connected neural networks,’ The Int. J. of Neural Systems, Vol.8, pp. 535-558, 1997.
[3] D. Novikov, R. V. Yampolskiy, and L. Reznik, “Artificial Intelligence Approaches for Intrusion Detection,’ Long Island Systems Applications and Technology Conf. (LISAT2006), Long Island, New York., May 5, 2006.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] A. Canning and E. Gardner, “Partially Connected Models of Neural Networks,’ J. of Physics, A Mathematical General, Vol.21, pp. 3275-3284, August 1988.

[2] [2] D. Elizondo and E. Fiesler, “A survey of partially connected neural networks,’ The Int. J. of Neural Systems, Vol.8, pp. 535-558, 1997.

[3] [3] D. Novikov, R. V. Yampolskiy, and L. Reznik, “Artificial Intelligence Approaches for Intrusion Detection,’ Long Island Systems Applications and Technology Conf. (LISAT2006), Long Island, New York., May 5, 2006.