A Deep Autoencoder Trust Model for Mitigating Jamming Attack in IoT Assisted by Cognitive Radio

Abstract

This article proposes deep learning (DL) framework constructed using deep autoencoder (DAE) to detect the malicious nodes in an Internet of Things (IoT) network assisted by the cognitive radio (CR) technology. In the IoT era, a plethora of nodes are connected to the network for the purpose of collecting and exchanging data. CR technology finds its role in IoT applications because of its ability to efficiently exploit the available spectrum. In this article, we consider IoT nodes as secondary users that perform cooperative spectrum sensing (CSS). Specifically, these IoT nodes sense the spectrum and send their reports to the fusion center (FC) to determine whether the spectrum is occupied by the primary user or not. In such a scenario, the existence of malicious IoT nodes might mislead the FC. To determine which of these IoT nodes are benevolent and which are malicious is crucial. We adopt a DAE-based DL framework, called DAE-TRUST, to detect malicious nodes in CR-assisted IoT. The proposed DAE-TRUST is able to identify the malicious nodes, whose reports can be excluded from the spectrum detection process. Simulation results, in six different real-world environments, show that our DAE-TRUST enhances the performance of CSS in IoT applications.