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

• Published in: EURASIP journal on wireless communications and networking Vol. 2023; no. 1; pp. 98 - 20
• Main Authors: Ezhilarasi, I. Evelyn, Clement, J. Christopher, Arul, Joseph M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 30.09.2023; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Blockchain; Classification; Cognitive radio; Communications Engineering; Cryptography; Data encryption; Deep learning; Engineering; Information Systems Applications (incl.Internet); Internet of Things; Jamming attack; Machine learning; Networks; Primary user emulation attack; Radio networks; Review; Signal,Image and Speech Processing; Spectrum sensing data falsification attack; Support vector machines; Deep learning; Primary user emulation attack; Jamming attack; Machine learning; Blockchain; Spectrum sensing data falsification attack
• ISSN: 1687-1499; 1687-1472; 1687-1499
• DOI: 10.1186/s13638-023-02290-z

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.