An innovative approach for dynamic key dependent S-Box to enhance security of IoT systems
The Internet of Things (IoT) foresees pervasive and connected smart nodes that interact independently while offering varied services. Today, IoT nodes are a treasure house of data and hence security has become a major concern in IoT networks. Conventional cryptography faces many challenges during im...
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Veröffentlicht in: | Measurement. Sensors 2023-12, Vol.30, p.100923, Article 100923 |
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Sprache: | eng |
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Zusammenfassung: | The Internet of Things (IoT) foresees pervasive and connected smart nodes that interact independently while offering varied services. Today, IoT nodes are a treasure house of data and hence security has become a major concern in IoT networks. Conventional cryptography faces many challenges during implementation in IoT devices. These challenges can be tackled by lightweight cryptography which is highly secure with less resource requirements. The vital characteristic of security is fulfilled by one out of 6 inner structures such as Substitution-Permutation Network, Feistel Network, General Feistel Network, Add-Rotate-XOR, Non Linear-Feedback Shift Register and Hybrid. PRESENT, which is a NIST approved lightweight cryptographic algorithm with SPN structure uses fixed or static S-Box. This permits attackers to explore S-Box and identify weaknesses of the algorithm. Key dependent approaches are better because it impedes an attacker from any offline analyses of attacks on the S-Box. Additionally, this is susceptible to linear and differential attacks. The static S Box does not possess superior diffusion and does not have a good rate of avalanche effect. So, the solution is to create a robust dynamic key dependent algorithm with nonlinear S-Boxes to secure the communication between IoT devices and platform. Security analysis is done on Non-linearity, Differential and Linear Cryptanalysis and Avalanche characteristics, besides, encryption of IoT data to demonstrate its aptness for cold chain security applications.
© 2017 Elsevier Inc. All rights reserved. |
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ISSN: | 2665-9174 2665-9174 |
DOI: | 10.1016/j.measen.2023.100923 |