Integrating Blockchain Deep Learning and Ensemble Methods for Optimized MQTT Protocol in IoT Networks

MQTT is a lightweight protocol used commonly that offers low power consumption and overhead, but suffers significant security threats such as Man-in-the-Middle (MiTM) attacks, Packet Sniffing, and network anomalies. The presented work introduces an enhanced MQTT protocol that integrates blockchain technology, deep learning, and ensemble methods in optimized ways connected with security and performance for IoT environments that are either resource-constrained or challenged. Blockchain technology ensures integrity of data and tamper-proof communication between IoT devices with brokers; Deep Learning models, including LSTM and CNNs, detect anomalies in real-time and prevent MiTM attacks. The techniques in Ensemble Learning which combine Random Forest and XGBoost together enhance the ability of detection of attacks, improving accuracies and reliability. The system is designed to be lightweight yet effective for securing MQTT communications, especially when it ensures authenticity of data while optimizing the network traffic load with minimal processing load. The experimental results find good improvements in network threat detection capabilities and the efficiency of data transmission. Such results confirm the possibility of bringing such technologies into an IoT framework. It develops more secure IoT networks, particularly for applications that require the handling of high security and optimized performance.