Deep Learning-Based Multiuser Classification for Malicious User Detection in 5G and Beyond Cooperative Sensing Systems
Ram S Iyer, Shivam Raj, Vaibhav Mishra, Shivanshu Shrivastava · 2024
Cognitive radio (CR)-based 5G and beyond networks have emerged as a potential technology, offering secondary users (SUs) the ability to utilize radio spectrum designated for licensed primary users (PUs) in their absence. Cooperative spectrum sensing (CSS) plays a crucial role in enhancing spectrum sensing accuracy within CR networks. However, the efficacy of CSS can be undermined by potential attacks from malicious users (MUs) transmitting inaccurate sensing information to the fusion center (FC). This study introduces a novel machine learning (ML) based scheme for identifying malicious users in CR networks before their data reaches the FC, thereby enabling the complete insulation of FC from adversarial signals generated by malicious users. Through simulations, the effectiveness of the proposed deep neural network (DNN) algorithm-based ML approach in detecting MUs is evaluated. The proposed DNN model overcomes the existing schemes by classifying multiple SUs before reaching the FC.