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A Lightweight, Secure Big Data-Based Authentication and Key-Agreement Scheme for IoT with Revocability.

Authors :
Zahednejad, Behnam
Teng, Huang
Kosari, Saeed
Xiaojun, Ren
Source :
International Journal of Intelligent Systems; 4/29/2023, p1-19, 19p
Publication Year :
2023

Abstract

With the rapid development of Internet of Things (IoT), designing a secure two-factor authentication scheme for IoT is becoming increasingly demanding. Two-factor protocols are deployed to achieve a higher security level than single-factor protocols. Given the resource constraints of IoT devices, other factors such as biometrics are ruled out as additional authentication factors due to their large overhead. Smart cards are also prone to side-channel attacks. Therefore, historical big data have gained interest recently as a novel authentication factor in IoT. In this paper, we show that existing big data-based schemes fail to achieve their claimed security properties such as perfect forward secrecy (PFS), key compromise impersonation (KCI) resilience, and server compromise impersonation (SCI) resilience. Assuming a real strong attacker rather than a weak one, we show that previous schemes not only fail to provide KCI and SCI but also do not provide real two-factor security and revocability and suffer inside attack. Then, we propose our novel scheme which can indeed provide real two-factor security, PFS, KCI, and inside attack resilience and revocability of the client. Furthermore, our performance analysis shows that our scheme has reduced modular exponentiation operation and multiplication for both the client and the server compared to Liu et al.'s scheme which reduces the execution time by one third for security levels of λ = 128. Moreover, in order to cope with the potential threat of quantum computers, we suggest using lightweight XMSS signature schemes which provide the desired security properties with λ = 128 bit postquantum security. Finally, we prove the security of our proposed scheme formally using both the real-or-random model and the ProVerif analysis tool. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08848173
Database :
Complementary Index
Journal :
International Journal of Intelligent Systems
Publication Type :
Academic Journal
Accession number :
164481486
Full Text :
https://doi.org/10.1155/2023/9731239