Title: Deep learning framework for early detection of intrusion in virtual environment

Authors: G. Madhu Priya; S. Mercy Shalinie; P. Mohana Priya

Addresses: Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu, India ' Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu, India ' Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu, India

Abstract: Today's business enterprise adapts cloud-based services as its architectural design. Intelligence technique incorporated into the architecture gives massive tangible and intangible benefits in terms of performance and reliability. Such cloud-based business architecture faces many threats towards its availability. DDoS attack is the most prominent threat as its impact is more in the virtual resource-based cloud infrastructure. Therefore, there is a need for a business intelligence-based framework to detect early the attack by monitoring the virtual network traffic. The proposed intelligence framework uses a deep learning framework, continuous discriminative-deep belief network (CD-DBN). CD-DBN dynamically captures attack patterns from the network data, analyses the data and detects the intrusion to the cloud. The observed result shows that the earlier detection approach guarantees the availability of cloud services to the legitimate users and enhances the cloud resource usage.

Keywords: deep learning; restricted Boltzmann machine; deep belief network; cloud environment; virtualisation; hypervisor; intrusion detection; availability threat; DDoS attack; SysBench benchmark suite.

DOI: 10.1504/IJBIDM.2020.109296

International Journal of Business Intelligence and Data Mining, 2020 Vol.17 No.3, pp.393 - 411

Received: 23 Nov 2017
Accepted: 25 Feb 2018
Published online: 03 Sep 2020 *

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