A survey on cognitive radio network attack mitigation using machine learning and blockchain

Abstract

Abstract Cognitive radio network is a promising technology to enhance the spectrum utilization and to resolve the spectrum scarcity issues. But the malicious users play havoc with the network during spectrum sensing and demean the network performance. It is mandatory to identify such malicious attacks and address it. There have been many traditional methods to mitigate the cognitive radio network attacks. In this paper, we have surveyed advanced attack mitigation techniques like machine learning, deep learning and blockchain. Thus, by detecting and addressing the malicious activities, the throughput and overall network performance can be improved.

Keywords

• Primary user emulation attack
• Spectrum sensing data falsification attack
• Jamming attack
• Machine learning
• Deep learning
• Blockchain