Model improvement and performance analysis of a fuzzy logic based secured MQTT protocol for IOT devices
Keywords:
MQTT, HTTP, Fuzzy Logic, IoT Security, Node-RED, BrokerAbstract
Ensuring secure communication among smart IoT devices remains a critical challenge due to the inherent vulnerabilities of lightweight messaging protocols. This study focuses on enhancing the Message Queuing Telemetry Transport (MQTT) protocol by embedding a fuzzy logic–based security mechanism within its publish–subscribe architecture. The proposed Fuzzy-Based Secured MQTT (FBSM) model introduces two lightweight traffic variables the Connection Message Ratio (CMR) and the Acknowledgment Message Ratio (AMR) as inputs for real-time anomaly detection. Unlike earlier approaches that rely on complex machine learning models or static thresholds, the novelty of this work lies in its minimalist fuzzy variable design, real-time deployment on Node-RED, and systematic benchmarking against both MQTT and HTTP protocols. Experimental evaluation using Wireshark and Node-RED demonstrates that the throughput values are 72.68, 73.67, 159.74, and 607.5 bytes per millisecond for FBSM compared to 65.6, 41.52, 21.098, and 17.24 bytes per millisecond for MQTT at message counts of 1, 10, 100, and 1000, respectively. However, from test, the latency of the FBSM system is seen to be 0.07 sec higher than the conventional MQTT as message is been transferred. These results confirm that the proposed model significantly enhances resistance to denial-of-service and congestion-based attacks while maintaining the lightweight advantages of MQTT. The study therefore provides a scalable and practically implementable security enhancement for IoT communication, with strong potential for application in smart homes, healthcare, and industrial systems
References
Farraposo, J. M., et al. (2005). “Denial of service attacks: Detection, prevention and mitigation.” Proceedings of the IEEE International Symposium on Communications and Information Technologies (ISCIT), pp. 517–522.
Granjal, J., Monteiro, E., & Silva, J. S. (2015). “Security for the Internet of Things: A survey of existing protocols and open research issues.” IEEE Communications Surveys & Tutorials, 17(3), pp. 1294–1312.
Gupta, B. B., & Quamara, M. (2018). “An overview of Internet of Things (IoT): Architectural aspects, security and privacy.” Journal of Organizational and End User Computing (JOEUC), 30(4), pp. 1–23.
HiveMQ Team. (n.d.). MQTT Essentials: Part 1. HiveMQ Documentation. Available at: https://www.hivemq.com/mqtt-essentials
Hunkeler, U., Truong, H. L., & Stanford-Clark, A. (2008). “MQTT-S - A publish/ subscribe protocol for Wireless Sensor Networks.” 2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE), pp. 791–798.
IBM & Eurotech. (2010). MQTT v3.1 Protocol Specification. OASIS Standard.
Kothmayr, T., Schmitt, C., Hu, W., Brunig, M., & Carle, G. (2013). “DTLS based security and two-way authentication for the Internet of Things.” Ad Hoc Networks, 11(8), pp. 2710–2723.
Mahmud, R., Ramachandran, M., & Buyya, R. (2020). “Application management in fog computing environments: A taxonomy, review, and future directions.” ACM Computing Surveys (CSUR), 53(4), 1–29.
Meidan, Y., et al. (2018). N‑BaIoT: Network‑based detection of IoT botnet attacks using deep autoencoders. arXiv preprint arXiv:1805.03409. techscience.comjaist.ac.jp+3arXiv+3ScienceDirect+3
Mitrokotsa, A., & Douligeris, C. (2007). “Detecting denial of service attacks in wireless networks using fuzzy logic.” Proceedings of the IEEE International Conference on Pervasive Services (ICPS), pp. 163–170.
Naik, N. (2017). “Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP.” 2017 IEEE International Systems Engineering Symposium (ISSE), pp. 1–7.
Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., Sadeghi, A.-R. (2018). DÏoT: A federated self-learning anomaly detection system for IoT. arXiv preprint arXiv:1804.07474. arXiv+1
Node-RED. (2016). Node-RED: Flow-based programming for the Internet of Things. IBM Emerging Technology. Available at: https://nodered.org
Node-RED-Contrib-Fuzzy. (2019). Fuzzy logic extension for Node-RED. Available at: https://flows.nodered.org/node/node-red-contrib-fuzzy
OASIS. (2019). MQTT Version 5.0 Specification. OASIS Standard. Available at: https://docs.oasis-open.org/mqtt/mqtt-/v5.0
Rahman, M. Z. U., et al. (2023). An IoT‑fuzzy intelligent approach for holistic management… [Healthcare fuzzy logic IoT]. PMCID: PMC10756970. PMC
Rizzardi, A., Sicari, S., Miorandi, D., & Cappiello, C. (2016). “A fuzzy logic-based approach to enforce security in the IoT.” Computer Communications, 89–90, 52–63.
Sciancalepore, S., Piro, G., Caldarola, D., Boggia, G., & Bianchi, G. (2017). “OAuth-IoT: An access control framework for the Internet of Things based on open standards.” 2017 IEEE Symposium on Computers and Communications (ISCC), pp. 676–681.
Singh, M., Rajan, M. A., Shivraj, V. L., & Balamuralidhar, P. (2015). “Secure MQTT for the Internet of Things (IoT).” 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT), pp. 746–751.
Thangavel, D., Ma, X., Valera, A., Tan, H. X., & Tan, C. K. Y. (2014). “Performance evaluation of MQTT and CoAP via a common middleware.” 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1–6.
Van den Bossche, P., Jacobs, J., & Gabrys, B. (2018). “Specifying an MQTT tree for a connected smart home.” Procedia Computer Science, 130, pp. 824–831.
Wang, Y. (2013). “Design and implementation of push notification system based on the MQTT protocol.” International Journal of Future Generation Communication and Networking (IJFGCN), 6(6), pp. 123–132.
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