Safeguarding IoT networks against DDoS attacks using deep learning based zero trust network access

Abstract Here, a deep learning‐based zero trust network access (DL‐ZTNA) system to enhance the security of the Message Queuing Telemetry Transport (MQTT) protocol within Internet of Things (IoT) applications was proposed. Combining multi‐head convolutional neural networks and attention‐based bi‐directional long short‐term memory networks with ZTNA provides real‐time security analysis of device behaviour. This behaviour‐based approach ensures that only authorized devices can access network resources and continuously monitors for potential threats like distributed denial of service (DDoS) attacks. The proposed DL‐ZTNA system revokes device access when a threat is detected and prevents further malicious activities. Evaluation in a testbed environment showed improvements in CPU usage efficiency, throughput, and attack detection probability compared to traditional methods. This highlights the system's effectiveness in securing MQTT‐based IoT networks against DDoS attacks while maintaining high performance, showcasing the potential of integrating deep learning techniques into ZTNA system for addressing security challenges in IoT environments.