Detection of SSDF Attack in Cooperative CR Networks with Machine Learning approach

DOI

10.2991/978-94-6463-662-8_82How to use a DOI?

Keywords

Spectrum Sensing; Machine Learning; Cognitive Radio Network; Byzantine Attack; spectrum sensing data falsification Attack

Abstract

Cognitive radio network is a Swireless network with transceivers that can intelligently detects which communication channels are in use and which are not in use. In a cognitive radio network, a transceiver uses intelligence to determine which communication channels are being used and which are not. By allowing secondary users to occasionally use spectrum allotted to primary users without disturbing them, Cognitive Radio Networks (CRNs) are intended to increase spectrum utilisation. Tosense the spectrumand to data transfer in this dynamic environment, CRNs depend on user cooperation. Malicious actions, such spectrum sensing data falsification (SSDF) attacks, can interfere with CRN functionality, resulting in data corruption, network inefficiencies, and missed spectrum opportunities. Byzantine assaults are another name for spectrum sensing data falsification (SSDF) attacks. This assault is a situation where malevolent nodes or attackers attempt to undermine the network's integrity by supplying inaccurate information. These attacks can happen in CRNs during spectrum sensing, where the attackers may trick the network into erroneously determining whether primary users are present or not. In the first place, this assault makes a group of benign nodes in any network malicious. The attacker then uses this group of nodes to take over the network. This group of nodes eventually turns into selfish nodes and is the cause of network data manipulation. This study suggests a unique method that uses decision trees and random forests to identify byzantine attacks in CR networks.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - C. Rajeswari
AU  - S. Saheb Basha
PY  - 2025
DA  - 2025/03/17
TI  - Detection of SSDF Attack in Cooperative CR Networks with Machine Learning approach
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 1050
EP  - 1057
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_82
DO  - 10.2991/978-94-6463-662-8_82
ID  - Rajeswari2025
ER  -