Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks
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 ra...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/13/4659 |
| _version_ | 1797408224347947008 |
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| author | Ernesto Cadena Muñoz Luis Fernando Pedraza Cesar Augusto Hernández |
| author_facet | Ernesto Cadena Muñoz Luis Fernando Pedraza Cesar Augusto Hernández |
| author_sort | Ernesto Cadena Muñoz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T03:55:19Z |
| format | Article |
| id | doaj.art-2551dc6193124f86825c03ca793fa0a1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:55:19Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2551dc6193124f86825c03ca793fa0a12023-12-03T14:21:30ZengMDPI AGSensors1424-82202022-06-012213465910.3390/s22134659Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio NetworksErnesto Cadena Muñoz0Luis Fernando Pedraza1Cesar Augusto Hernández2Technological Faculty, Universidad Distrital Francisco José de Caldas, Bogotá 111931, ColombiaTechnological Faculty, Universidad Distrital Francisco José de Caldas, Bogotá 111931, ColombiaTechnological Faculty, Universidad Distrital Francisco José de Caldas, Bogotá 111931, ColombiaMobile 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.https://www.mdpi.com/1424-8220/22/13/4659mobile cognitive radio networkspectrum sensingsoftware-defined radioprimary user emulation |
| spellingShingle | Ernesto Cadena Muñoz Luis Fernando Pedraza Cesar Augusto Hernández Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks Sensors mobile cognitive radio network spectrum sensing software-defined radio primary user emulation |
| title | Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks |
| title_full | Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks |
| title_fullStr | Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks |
| title_full_unstemmed | Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks |
| title_short | Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks |
| title_sort | machine learning techniques based on primary user emulation detection in mobile cognitive radio networks |
| topic | mobile cognitive radio network spectrum sensing software-defined radio primary user emulation |
| url | https://www.mdpi.com/1424-8220/22/13/4659 |
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