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Optimization of Adaptive Resonance Theory Neural Network Using Particle Swarm Optimization Technique

  • Khushboo Satpute
  • Rishik Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

Abstract

With the advancement of computers and its computational enhancement over several decades of use, but with the growth in the dependencies and use of these systems, more and more concerns over the risk and security issues in networks have raised. In this paper, we have proposed approach using particle swarm optimization to optimize ART. Adaptive resonance theory is one of the most well-known machine-learning-based unsupervised neural networks, which can efficiently handle high-dimensional dataset. PSO on the other hand is a swarm intelligence-based algorithm, efficient in nonlinear optimization problem and easy to implement. The method is based on anomaly detection as it can also detect unknown attack types. PSO is used to optimize vigilance parameter of ART-1 and to classify network data into attack or normal. KDD ’99 (knowledge discovery and data mining) dataset has been used for this purpose.

Keywords

Intrusion detection system Adaptive resonance theory 1 Particle swarm optimization 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.MCOEPuneIndia
  2. 2.NITKSurathkalIndia

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