An Optimal NIDS for VCN Using Feature Selection and Deep Learning Technique: IDS for VCN

Pankaj Kumar Keserwani, Mahesh Chandra Govil, E. S. Pilli, Prajjval Govil

Source Title: International Journal of Digital Crime and Forensics (IJDCF)13(6)

ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799867531|DOI: 10.4018/IJDCF.20211101.oa10

MLA

Keserwani, Pankaj Kumar, et al. "An Optimal NIDS for VCN Using Feature Selection and Deep Learning Technique: IDS for VCN." IJDCF vol.13, no.6 2021: pp.1-25. http://doi.org/10.4018/IJDCF.20211101.oa10

APA

Keserwani, P. K., Govil, M. C., Pilli, E. S., & Govil, P. (2021). An Optimal NIDS for VCN Using Feature Selection and Deep Learning Technique: IDS for VCN. International Journal of Digital Crime and Forensics (IJDCF), 13(6), 1-25. http://doi.org/10.4018/IJDCF.20211101.oa10

Chicago

Keserwani, Pankaj Kumar, et al. "An Optimal NIDS for VCN Using Feature Selection and Deep Learning Technique: IDS for VCN," International Journal of Digital Crime and Forensics (IJDCF) 13, no.6: 1-25. http://doi.org/10.4018/IJDCF.20211101.oa10

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Abstract

In this modern era, due to demand for cloud environments in business, the size, complexity, and chance of attacks to virtual cloud network (VCN) are increased. The protection of VCN is required to maintain the faith of the cloud users. Intrusion detection is essential to secure any network. The existing approaches that use the conventional neural network cannot utilize all information for identifying the intrusions. In this paper, the anomaly-based NIDS for VCN is proposed. For feature selection, grey wolf optimization (GWO) is hybridized with a bald eagle search (BES) algorithm. For classification, a deep learning approach - deep sparse auto-encoder (DSAE) is employed. In this way, this paper proposes a NIDS model for VCN named - GWO-DES-DSAE. The proposed system is simulated in the python programming environment. The proposed NIDS model's performance is compared with other recent approaches for both binary and multi-class classification on the considered datasets - NSL-KDD, UNSW-NB15, and CICIDS 2017 and found better than other methods.