Multi-Level Encryption Framework

Multi-level encryption approaches are becoming more popular as they combine the strength of multiple basic/traditional approaches into a complex one. Many multi-level encryption approaches have been introduced for different systems, like Internet of Things, sensor networks, big data, and the web. Th...

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Veröffentlicht in:International journal of advanced computer science & applications Jg. 9; H. 4
1. Verfasser: Habboush, Ahmad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: West Yorkshire Science and Information (SAI) Organization Limited 2018
Schlagworte:
Algorithms
Avalanches
Computational efficiency
Genetic algorithms
Internet of Things
Performance measurement
Run time (computers)
ISSN:2158-107X, 2156-5570
Online-Zugang:Volltext
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Zusammenfassung:Multi-level encryption approaches are becoming more popular as they combine the strength of multiple basic/traditional approaches into a complex one. Many multi-level encryption approaches have been introduced for different systems, like Internet of Things, sensor networks, big data, and the web. The main obstacles in building such approaches are to have a secure as well as a computationally efficient multi-level encryption approach. In this paper, we propose a computationally efficient multi-level encryption framework that combines the strength of symmetric, the encryption algorithm AES (Advance Encryption Standard), Feistel network, Genetic Algorithm’s Crossover and Mutation techniques, and HMAC. The framework was evaluated and compared to a set of benchmark symmetric encryption algorithms, such as RC5, DES, and 3-DES. The evaluation was carried out on an identical platform and the algorithms were compared using the throughput and running time performance metrics and Avalanche effect security metric. The results show that the proposed framework can achieve the highest throughput and the lowest running time compared to the considered benchmarked symmetric encryption algorithms and passes the avalanche effect criterion.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2018.090422