Deep Learning-Based Double Authentication Protection for MQTT Transactions in Iiot Environment Against Detected Dos Variants

Abstract

Recently, there has been a great deal of applicability for fast message transmission protection for Industrial Internet of Things (IIoT) applications employing the double authentication paradigm. Of course, malicious people are naturally drawn to any new technology that is widely used by industry and would stop at nothing to take full advantage of it by employing cutting-edge technologies such as Denial of Service (DoS). MQTT, or message queuing telemetry transport, is said to be the easiest protocol for Industrial IoT devices to use. MQTT Protocol-based message transmission protection using double or triple authentication method to secure the MQTT messages from detected DoS attack Variants in IoT. Preprocessing, feature extraction, and hybrid network are the three components of the LHMDA-DL approach that enable secure MQTT message transfer from identified DoS variants. When compared to current approaches, the results show a significant improvement in processing time, traffic overhead, message transmission accuracy, and message transmission error rate.