Anomaly-Based Intrusion Detection System using Supervised Learning Algorithm Artificial Neural Network and Ant Colony Optimization with Feature Selection
Annu Raj1, Monika poriye2

1Miss. Annu Raj, Assistant professor, Vaish College of Engineering, Rohtak.
2Mrs Monika Poriye, Assistant Professor, Department of Computer Science and Applications, Kurukshetra University Kurukshetra.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2475-2481 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5683029320/2020©BEIESP | DOI: 10.35940/ijeat.C5683.029320
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Abstract: In the advent of the cyber world, all know that cyber security is randomly used research area for researchers to secure host, network, and data because of increasingly complex attacks. In the advent of anomaly-based intrusion detection system, various techniques are applied to detect intrusion on system or network. This approach attains an extreme detection rate and accuracy but there may be overhead acquired to build and training them. The objective of this paper is to detect the intrusion of a system by proposing a Data mining technique which is based on supervised learning algorithm for training dataset. Artificial neural network (ANN) and Ant Colony Optimization (ACO) with feature selection are the basics of the proposed scheme. ACO work on a population-based algorithm and is motivated by the pheromone trail laying behavior of real ants, in which NSL-KDD Cup99 Dataset is used. Empirical Results clearly explain that the proposed system can attain an overall detection rate of 88% and time complexity of 0.343 sec, which is satisfactory when compared to other anomaly-based schemes.
Keywords: Ant colony Optimization, PSO, Detection Rate, False alarm, Data mining, KDD Cup99, Confusion matrix, information security, firewall security