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An Innovative Instruct Dynamic Intrusion Detection System With Particle Swarm Optimization and Radial Basis Functions

  • Unique Paper ID: 162062
  • Volume: 10
  • Issue: 7
  • PageNo: 297-304
  • Abstract:
  • This research paper is an elaboration of Incremental Radial Based Function Neural Network model with Particles Swarm Optimization (IRBF-PSO) in Intrusion Detection System. This system is helpful to find the most featured misuse and anomaly detection. RBF network is most popular real-time classifier method. RBF method comprises of mostly analysis and the thorny part is finding the right weights and bias values for dynamic systems. The intrusion detection system has become highly dynamic. Many large or small enterprise systems are still facing with different problems in this area with dynamic form. So the main objective of my work is to employ Particles Swarm Optimization to detect the right weight and bias values for RBF method. In this method, apart from training with existing data and information for design, there is a need to extend or redesign the existing system to identify different pattern types and modulate the system using PSO with new patterns. After experimentation, this method has improved to identify the difficulty in anomaly detections and reduce the rate of false alarm and fail cases.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 7
  • PageNo: 297-304

An Innovative Instruct Dynamic Intrusion Detection System With Particle Swarm Optimization and Radial Basis Functions

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