PMID- 35808156
OWN - NLM
STAT- MEDLINE
DCOM- 20220712
LR  - 20220716
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 13
DP  - 2022 Jun 21
TI  - Machine Learning Techniques Based on Primary User Emulation Detection in Mobile 
      Cognitive Radio Networks.
LID - 10.3390/s22134659 [doi]
LID - 4659
AB  - Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile 
      communication because of the spectrum scarcity in actual mobile technologies such 
      as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to 
      transmit its signals. It is essential to detect the use of a radio spectrum 
      frequency, which is where the spectrum sensing is used to detect the PU presence 
      and avoid interferences. In this part of cognitive radio, a third user can affect 
      the network by making an attack called primary user emulation (PUE), which can 
      mimic the PU signal and obtain access to the frequency. In this paper, we applied 
      machine learning techniques to the classification process. A support vector 
      machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect 
      the PUE in simulation and emulation experiments implemented on a software-defined 
      radio (SDR) testbed, showing that the SVM technique detected the PUE and 
      increased the probability of detection by 8% above the energy detector in low 
      values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest 
      techniques in the experiments.
FAU - Munoz, Ernesto Cadena
AU  - Munoz EC
AUID- ORCID: 0000-0002-1086-3665
AD  - Technological Faculty, Universidad Distrital Francisco Jose de Caldas, Bogota 
      111931, Colombia.
FAU - Pedraza, Luis Fernando
AU  - Pedraza LF
AD  - Technological Faculty, Universidad Distrital Francisco Jose de Caldas, Bogota 
      111931, Colombia.
FAU - Hernandez, Cesar Augusto
AU  - Hernandez CA
AUID- ORCID: 0000-0001-9409-8341
AD  - Technological Faculty, Universidad Distrital Francisco Jose de Caldas, Bogota 
      111931, Colombia.
LA  - eng
GR  - 757/MinCiencias/
PT  - Journal Article
DEP - 20220621
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Cognition
MH  - *Machine Learning
MH  - Radio Waves
MH  - Software
MH  - *Support Vector Machine
PMC - PMC9269067
OTO - NOTNLM
OT  - mobile cognitive radio network
OT  - primary user emulation
OT  - software-defined radio
OT  - spectrum sensing
COIS- The authors declare no conflict of interest.
EDAT- 2022/07/10 06:00
MHDA- 2022/07/14 06:00
PMCR- 2022/06/21
CRDT- 2022/07/09 01:23
PHST- 2022/05/17 00:00 [received]
PHST- 2022/06/07 00:00 [revised]
PHST- 2022/06/16 00:00 [accepted]
PHST- 2022/07/09 01:23 [entrez]
PHST- 2022/07/10 06:00 [pubmed]
PHST- 2022/07/14 06:00 [medline]
PHST- 2022/06/21 00:00 [pmc-release]
AID - s22134659 [pii]
AID - sensors-22-04659 [pii]
AID - 10.3390/s22134659 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Jun 21;22(13):4659. doi: 10.3390/s22134659.