1. bookVolume 31 (2021): Issue 4 (December 2021)
    Advanced Machine Learning Techniques in Data Analysis (special section, pp. 549-611), Maciej Kusy, Rafał Scherer, and Adam Krzyżak (Eds.)
Journal Details
License
Format
Journal
eISSN
2083-8492
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English
access type Open Access

An ANN-based scalable hashing algorithm for computational clouds with schedulers

Published Online: 30 Dec 2021
Page range: 697 - 712
Received: 31 Jan 2021
Accepted: 10 Aug 2021
Journal Details
License
Format
Journal
eISSN
2083-8492
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English
Abstract

The significant benefits of cloud computing (CC) resulted in an explosion of their usage in the last several years. From the security perspective, CC systems have to offer solutions that fulfil international standards and regulations. In this paper, we propose a model for a hash function having a scalable output. The model is based on an artificial neural network trained to mimic the chaotic behaviour of the Mackey–Glass time series. This hashing method can be used for data integrity checking and digital signature generation. It enables constructing cryptographic services according to the user requirements and time constraints due to scalable output. Extensive simulation experiments are conduced to prove its cryptographic strength, including three tests: a bit prediction test, a series test, and a Hamming distance test. Additionally, flexible hashing function performance tests are run using the CloudSim simulator mimicking a cloud with a global scheduler to investigate the possibility of idle time consumption of virtual machines that may be spent on the scalable hashing protocol. The results obtained show that the proposed hashing method can be used for building light cryptographic protocols. It also enables incorporating the integrity checking algorithm that lowers the idle time of virtual machines during batch task processing.

Keywords

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