Improved Intrusion Detection System with Optimization Enabled Deep Neural Networks
Shijoe Jose1, D. Malathi2, Dorathi Jayaseeli3
1Shijoe Jose, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
2D. Malathi, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
3Dorathi Jayaseeli, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1092-1097 | Volume-8 Issue-11S September 2019 | Retrieval Number: K122209811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1222.09811S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cyber-crimes are prevailing at the extreme in the today’s technical world as the massive usage of the internet is on the peak among the world users, raising the security and privacy concerns. Thus, the paper concentrates on the intrusion detection mechanism in the networks, which is performed using the optimization-based deep belief neural networks (DBN). Input data is classified using the DBN classifier and the complexity associated with the classification is relieved through the feature selection strategy for which the Bhattacharya distance is employed. The DBN training is performed using Levenberg–Marquardt (LM) algorithm and Bird Swarm Algorithm (BSA), which is decided based on the minimal mean square error. The intrusion detection affords the security and privacy to the data. The analysis of the methods is presented using the KDD cup dataset and the comparative analysis is performed based on the accuracy, sensitivity, and specificity. The accuracy, sensitivity, and specificity of the BSA-DBN approach of intrusion detection are found to be 96.45%, 94.07%, and 96%, respectively.
Keywords: Intrusion Detection, Neural networks, Deep Belief network, Bhattacharya Distance.
Scope of the Article: Discrete Optimization