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

Autor:	I. Evelyn Ezhilarasi, J. Christopher Clement, Joseph M. Arul
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Primary user emulation attack Spectrum sensing data falsification attack Jamming attack Machine learning Deep learning Blockchain Telecommunication TK5101-6720 Electronics TK7800-8360
Zdroj:	EURASIP Journal on Wireless Communications and Networking, Vol 2023, Iss 1, Pp 1-20 (2023)
Druh dokumentu:	article
ISSN:	1687-1499
DOI:	10.1186/s13638-023-02290-z
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/fba6fa672ec1408e9481a7af986f7f24 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.