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.