Securing Industrial IoT: A Novel Approach with MQTT Authentication
As Message Queuing Telemetry Transport (MQTT) becomes a widely adopted protocol for IoT communication, the use of traditional Transport Layer Security (TLS) encryption poses challenges due to its high computational demands on resource-constrained devices and one-way authentication. In this paper, we propose a novel lightweight mutual authentication protocol designed for MQTT-based communication in Industrial Internet of Things (IIoT) environments. Our protocol ensures message integrity without the high computational overhead associated with TLS encryption. By leveraging an alternative Authenticated Encryption (AE) method optimized for resourceconstrained devices, we enhance both security and efficiency. To evaluate the performance, we conducted experiments using three Raspberry Pi devices, comparing our protocol to both non-encrypted and TLS-secured MQTT. The results demonstrate a significant reduction in system run-time, from 139 ms to 108 ms, when using the proposed protocol for mutual authentication, compared to default TLS, which only supports oneway authentication. Additionally, simulations conducted in NS3, across various practical scenarios, reveal notable improvements in authentication success rates and reduced delays. Our findings suggest that this protocol offers a promising solution for secure and efficient communication in IIoT systems.
Authors
- Ygal Bendavid (ORCID: https://orcid.org/0000-0003-2545-532X)
- Peyman Pahlevani (ORCID: https://orcid.org/0000-0001-5918-7250)
- Samad Rostampour (ORCID: https://orcid.org/0000-0003-0183-5993)
- Nasour Bagheri (ORCID: https://orcid.org/0000-0002-6818-5342)
- Amir Javadi (ORCID: https://orcid.org/0000-0001-7715-4583)
- Sadegh Sadeghi (ORCID: https://orcid.org/0000-0002-1125-6867)
Institutions
- Institute for Advanced Studies in Basic Sciences (IR)
- Vanier College (CA)
- University of Tehran (IR)
- X-Fab (Germany) (DE)
Publication Details
- Published
- 2025-06-08
- DOI
- https://doi.org/10.1109/icc52391.2025.11161120
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00