An Intelligent Feature Selection with Optimal Neural Network Based Network Intrusion Detection System for Cloud Environment
A. Thirumalairaj1, M. Jeyakarthic2
1A. Thirumalairaj*, Assistant Professor, Department of Computer Science, Kunthavai Naacchiyaar Govt Arts College for Women, Thanjavur.
2Dr. M. Jeyakarthic, Assistant Director (Academic), Tamil Virtual Academy, Chennai.
Manuscript received on January 23, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3560-3569 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6343029320/2020©BEIESP | DOI: 10.35940/ijeat.C6343.029320
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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: At present times, Cloud Computing (CC) becomes more familiar in several domains such as education, media, industries, government, and so on. On the other hand, uploading sensitive data to public cloud storage services involves diverse security issues, specifically integrity, availability and confidentiality to organizations/companies. Besides, the open and distributed (decentralized) structure of the cloud is highly prone to cyber attackers and intruders. Therefore, it is needed to design an intrusion detection system (IDS) for cloud environment to achieve high detection rate with low false alarm rate. The proposed model involves a binary grasshopper optimization algorithm with mutation (BGOA-M) as a feature selector to choose the optimal features. For classification, improved particle swarm optimization (IPSO) based NN model, called IPSO-NN has been derived. The significance of the IPSO-NN model is assessed using a set of two benchmark IDS dataset. The experimental results stated that the IPSO-NN model has achieved maximum accuracy values of 99.36% and 97.80% on the applied NSL-KDD 2015 and CICIDS 2017 dataset. The obtained experimental outcome clearly pointed out the extraordinary detection performance of the IPSO-NN model over the compared methods.
Keywords: Cloud computing, Intrusion, Detection, Feature Selection, Neural Network.