The rapid integration of the IoT devices into everyday decision-making processes underscores the need for continuous user authentication and data integrity checking during network communication, all while minimizing energy consumption to extend device lifespan. This article introduces the bio-integrated hybrid timed-efficient stream loss-tolerant authentication (TESLA) protocol, which is a fully symmetric and energy-efficient authentication protocol designed for the resource-constrained IoT devices. Based on the hybrid TLI- $\mu $ TESLA protocol, this innovative solution prioritizes high cybersecurity levels and minimal computational requirements for continuous authentication. An innovative advancement involves eliminating the public cryptography process during the synchronization stage of the TESLA protocols. Instead, biometric authentication through the distorted fingerprint and electroencephalogram templates is employed, to establish a nonshared symmetric session key, utilized only once. Furthermore, neither the key nor the original biometric templates are transmitted over the network, ensuring user identity preservation and effectively resolving the key distribution challenge inherent in symmetric cryptography. By offloading intensive tasks to the servers and avoiding the storage or transmission of biometric data, the proposed approach conserves the IoT device energy and enhances cybersecurity. Simulation analyses and cybersecurity assessments demonstrate successful synchronization, privacy preservation, and low computational demands compared to the existing protocols, making the bio-integrated hybrid TESLA protocol a significant advancement in IoT authentication.
Bio-Integrated Hybrid TESLA: A Fully Symmetric Lightweight Authentication Protocol
K. Eledlebi,A. Alzubaidi,Ernesto Damiani,Deepak Puthal,Victor Mateu,Mohammed Jamal Zemerly,Yousof Al-Hammadi,C. Yeun
Published 2024 in IEEE Internet of Things Journal
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- Publication year
2024
- Venue
IEEE Internet of Things Journal
- Publication date
2024-12-01
- Fields of study
Computer Science, Engineering, Environmental Science
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