• Amit Kumar
• Basant Kumar
• Devendra Kumar Singh

Intrusion detection system research field has grown tremendously in the past decade. Many people have proposed various models to improve performance of attack classification. Intrusion detection system play a vital role in network security, its check the system for integrity, confidentiality and availability in an individual manner like anomaly intrusion detection and misuse intrusion detection. The main objective of this paper is to develop a hybrid model for intrusion detection system so that achieve the best detection accuracy performance. We have given the idea of classification based on optimization and feature selection using Particle Swarm Optimization (PSO) for discriminating features selection and Backpropagation neural network (BPNN) for classification of attack on Network. This BPNN-PSO Model has experimented on KDD-CUP’99 to achieve maximum detection rate with minimum false alarm rate.

"Intrusion Detection System Using Particle Swarm Optimization and Back Propagation Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.550-557, May-2019, Available :http://www.jetir.org/papers/JETIR1905482.pdf

"Intrusion Detection System Using Particle Swarm Optimization and Back Propagation Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp550-557, May-2019, Available at : http://www.jetir.org/papers/JETIR1905482.pdf