Machine Learning-based Primary User Emulation Attack Detection In Cognitive Radio Networks using Pattern Described Link-Signature (PDLS)
| Autor: | Vijay Devabhaktuniz, Abdulsahib Albehadili, Ahmad Y. Javaid, Farha Jahan, Jared Oluochy, Atif Ali |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Emulation
business.industry Computer science Orthogonal frequency-division multiplexing ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Testbed Transmitter Physical layer 020206 networking & telecommunications 020302 automobile design & engineering 02 engineering and technology Machine learning computer.software_genre Cognitive radio 0203 mechanical engineering 0202 electrical engineering electronic engineering information engineering Wireless Artificial intelligence business computer Multipath propagation |
| Zdroj: | WTS |
| DOI: | 10.1109/wts.2019.8715527 |
| Popis: | A machine learning (ML) framework is proposed for primary user emulation attack (PUEA) detection in Cognitive Radio Networks (CRN). The ML framework is based on various classification models that exploit features extracted using the proposed Pattern Described Link-Signature method (PDLS) to distinguish between legitimate and malicious users. PDLS defines Link-signature from the channel impulse response (CIR) of the wireless link in the multipath environment. Previous works define Link-signature as the amplitude (or power) value of CIR while PDLS, on the other hand, defines it as a pattern that describes the structure of 52 sub-CIR (i.e., CIR contains 52 sub-CIR) in Orthogonal Frequency Division Multiplexing (OFDM) based transceivers. The proposed scheme is tested by developing a Software-Defined Radio (SDR) testbed to capture real wireless channel measurements. The testbed is based on IEEE 802.11a/g/p standard transceiver which comprises OFDM physical layer. Experimental results show that the proposed approach can distinguish between legitimate and malicious users effectively. |
| Databáze: | OpenAIRE |
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