Performance comparison between backpropagation algorithms applied to intrusion detection in computer network systems

Authors:
Iftikhar Ahmad;M. A. Ansari;Sajjad Mohsin
Affiliations:
Department of Computer Sciences, Federal Urdu University, Islamabad & COMSATS Institute of Information Technology, Pakistan;Department of Computer Sciences, Federal Urdu University, Islamabad & COMSATS Institute of Information Technology, Pakistan;Department of Computer Sciences, Federal Urdu University, Islamabad & COMSATS Institute of Information Technology, Pakistan
Venue:
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
Year:
2008

Citing 3
Cited 1

An Intrusion-Detection Model

IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Parameter Incremental Learning Algorithm for Neural Networks

IEEE Transactions on Neural Networks

Classification of GPS Satellites Using Improved Back Propagation Training Algorithms

Wireless Personal Communications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a topology of neural network intrusion detection system is proposed on which different backpropagation algorithms are benchmarked. The proposed methodology uses sampled data from KddCup99 data set, an intrusion detection attacks database that is a standard for the evaluation of intrusion detection systems. The performance of backpropagation algorithms implemented in batch mode, is addressed. A comparative analysis of algorithms is made and then the most optimum solution is selected with respect to mean square error.