Hashed Needham Schroeder Industrial IoT based Cost Optimized Deep Secured data transmission in cloud. (January 2020)

• Record Type: Journal Article
• Authors: Alzubi, Jafar A.; Manikandan, Ramachandran; Alzubi, Omar A.; Qiqieh, Issa; Rahim, Robbi; Gupta, Deepak; Khanna, Ashish
• Is Part Of: Measurement. Volume 150(2020)
• Publication Date: 2020-01
• Subjects: Hashed Needham Schroeder -- Cost Optimized -- Deep Machine Learning -- Public Key Generation -- Anticipated flags -- Non-anticipated flags; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107077
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The secret IoT data transmission using Hashed Needham-Shroeder has been investigated. Each cloud user can send or receive the data/messages solely based on its observation. Asymmetric cryptography, HNS, Cost Optimized Deep Secured data transmission has been used. The result shows that the proposed method is beneficial to IIoT. Secured transmission takes place with minimum computation cost and overhead. Abstract: Deep learning is an encouraging approach for extracting precise information from raw sensor data from IoT devices. In this paper, Hashed Needham Schroeder (HNS) Cost Optimized Deep Machine Learning (HNS-CODML) method for secure Industrial IoT data transmissions via cloud environment has been proposed by indicating the necessity of providing Industrial IoT security using machine learning technique. First, HNS Public Key Generation (PKG) mechanism computes the public key and a flag value, then using public key, the execution time has been improved as only authenticated cloud users (CU) are allowed to exchange the data/messages via secured channel and can be trained; thus the cost function can be computed using two passes. In the first pass, the cost function has been measured while in second pass, the overall cost function is obtained, therefore reducing the computational cost (CC) and communication overhead (CO), making the entire process much easier to monitor and control.