AI Powered Threat Detection Framework for Security Enhancement of MQTT based IoT Networks

The Message Queuing Telemetry Transport (MQTT) protocol is widely adopted in IoT networks due to its lightweight and scalable design, making it ideal for resource-constrained devices. However, its minimal architecture lacks built-in encryption and robust identity authentication, leaving it vulnerable to a range of security threats. To address these vulnerabilities, this work proposes a hybrid framework that integrates machine learning (ML) and deep learning (DL) models to enhance intrusion and anomaly detection in MQTT based IoT networks. The framework leverages real world IoT traffic data from the MQTTEEB-D dataset with diverse preprocessing techniques to train the model. The framework is implemented and evaluated through a multi-metric approach, where each metric assesses a distinct aspect of the framework to identify lightweight processing models for resource-constrained environments. Results consistently showed that ML models outperformed their DL counterparts in terms of detection reliability, classification balance, and error minimization. While DL models demonstrated moderate effectiveness in capturing temporal patterns, they exhibited higher misclassification rates and reduced calibration. The findings underscore the effectiveness of lightweight ML models for scalable and dependable intrusion detection in MQTT based IoT networks.