doi: 10.7763/IJFCC.2015.V4.402
A Novel Incremental Instruct Dynamic Intrusion Detection System Using PSO-RBF
M. V. Siva Prasad
1
,
Ravi Gottipati
2
- 1 Anurag Engineering College, Kodad 508206, India.
- 2 Tripod Technologies, Hyderabad 500082, India.
Abstract
This research paper is an elaboration ofIncremental Radial Based Function Neural Network modelwith Particles Swarm Optimization (IRBF-PSO) in IntrusionDetection System. This system is helpful to find the mostfeatured misuse and anomaly detection. RBF network is mostpopular real-time classifier method. RBF method comprises ofmostly analysis and the thorny part is finding the right weightsand bias values for dynamic systems. The intrusion detectionsystem has become highly dynamic. Many large or smallenterprise systems are still facing with different problems inthis area with dynamic form. So the main objective of my workis to employ Particles Swarm Optimization to detect the rightweight and bias values for RBF method.<br>In this method, apart from training with existing data andinformation for design, there is a need to extend or redesign theexisting system to identify different pattern types and modulatethe system using PSO with new patterns. After experimentation,this method has improved to identify the difficulty in anomalydetections and reduce the rate of false alarm and fail cases.
Keywords
- Incremental method
- intrusion detectionsystem
- particles swarm optimization and radial based
How to Cite
M. V. Siva Prasad and Ravi Gottipati, "A Novel Incremental Instruct Dynamic Intrusion Detection System Using PSO-RBF," International Journal of Future Computer and Communication, vol. 4, no. 4, pp. 280-285, 2015. https://doi.org/ 10.7763/IJFCC.2015.V4.402
Copyright & License
Copyright © 2015 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).