Classification of Intrusion using Artificial Neural Network with GWO
Gurbani Kaur1, Dharmender Kumar2

1Gurbani Kaur*, M.Tech Scholar, Department of Computer Science and Engineering,Guru Jambheshwar University of Science &Technology,Hisar, India.
2Dharmender Kumar, Professor, Department of Computer Science and Engineering,Guru Jambheshwar University of Science & Technology, Hisar, India.

Manuscript received on March 05, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on April 30, 2020. | PP: 599-606 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7597049420/2020©BEIESP | DOI: 10.35940/ijeat.D7597.049420
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Abstract: In the present milieu of connected world, where security is the major concern, Intrusion Detection System is the prominent area of research to deal with various types of attacks in network. Intrusion detection systems (IDS) finds the dynamic and malicious traffic of network, in accordance to the aspect of network. Various form of IDS has been developed working on distinctive approaches. One popular approach is machine learning in which various algorithms like ANN, SVM etc. have been used. But the most prominent method used is ANN. The performance of the ANN can significantly be improved by combining it with different metaheuristic algorithms. In present work, GWO is used to optimize ANN. For this KDD-99 data-set is used to classify various types of attacks i.e. denial of service (DOS), normal and other form of attack. The present paper provides detailed analysis of the performance of Artificial Neural Network and optimized Artificial Neural Network with GA, PSO and GWO. The research shows that ANN with GWO outperform as compared to others (ANN, ANN with PSO and ANN with GA). 
Keywords: ANN, DOS, GA, GWO, IDS, KDD, PSO.