Deep-Learning-Based Blockchain Framework for Secure Software-Defined Industrial Networks

Maninderpal Singh

doi:10.1109/TII.2020.2968946

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Deep-Learning-Based Blockchain Framework for Secure Software-Defined Industrial Networks

2021-01-01

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Maninderpal Singh

Gagangeet Singh Aujla

Amritpal Singh

Neeraj Kumar *

Sahil Garg

DOI：10.1109/TII.2020.2968946

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Abstract

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Software-defined industrial network has emer-ged as an autonomous ecosystem where the network control relies on a centralized controller to provide seamless data transfer. However, the reliance on a centralized controller can lead to several challenges, such as single point of failure. An adversary can initiate a denial of service attack and limit the availability of the controller by projecting malicious or uncontrolled traffic flows. To overcome this, in this article, a deep-learning-based blockchain framework is designed for providing secure software-defined industrial network. In this framework, a blockchain mechanism is designed wherein all the switch are registered, verified (using zero-knowledge proof), and, thereafter, validated in the blockchain using a voting-based consensus mechanism. A deep Boltzmann machine based flow analyzer is deployed at the control plane to identify the anomalous switch requests. The evaluation is performed using a mininet emulator wherein the results obtained depict the superiority of the proposed framework.

Keywords:

Blockchain

Switches

Computer crime

Software

Informatics

Blockchain

deep learning

industrial networks

software-defined networking (SDN)

security

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