Sensing-Throughput tradeoff with primary user traffic and cooperative sensing in cognitive radio

Yousef, Elsayed M.; Soliman, Heba Y.; Ghuniem, Atef M.

doi:10.1109/ccoms.2017.8075280

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…An additive white Gaussian noise (AWGN) channel is assumed, and we use a time-based energy detection method as in [26]. N is the total number of samples of the energy and Y(n) is added during one S i detection interval, as shown in Equation (6).…”

Section: Energy Calculationmentioning

confidence: 99%

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Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

2020

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Mobile cognitive radio networks provide a new platform to implement and adapt wireless cellular communications, increasing the use of the electromagnetic spectrum by using it when the primary user is not using it and providing cellular service to secondary users. In these networks, there exist vulnerabilities that can be exploited, such as the malicious primary user emulation (PUE), which tries to imitate the primary user signal to make the cognitive network release the used channel, causing a denial of service to secondary users. We propose a support vector machine (SVM) technique, which classifies if the received signal is a primary user or a malicious primary user emulation signal by using the signal-to-noise ratio (SNR) and Rényi entropy of the energy signal as an input to the SVM. This model improves the detection of the malicious attacker presence in low SNR without the need for a threshold calculation, which can lead to false detection results, especially in orthogonal frequency division multiplexing (OFDM) where the threshold is more difficult to estimate because the signal limit values are very close in low SNR. It is implemented on a software-defined radio (SDR) testbed to emulate the environment of mobile system modulations, such as Gaussian minimum shift keying (GMSK) and OFDM. The SVM made a previous learning process to allow the SVM system to recognize the signal behavior of a primary user in modulations such as GMSK and OFDM and the SNR value, and then the received test signal is analyzed in real-time to decide if a malicious PUE is present. The results show that our solution increases the detection probability compared to traditional techniques such as energy or cyclostationary detection in low SNR values, and it detects malicious PUE signal in MCRN.

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Section: Energy Calculationmentioning

confidence: 99%

“…The energy detector compares Z to a threshold λ and makes a decision regarding the presence or absence of signals. The probability of detection and the probability of false alarm for energy detection is defined in Equation ( 7) [26].…”

Section: Energy Calculationmentioning

confidence: 99%

Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

2020

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“…H1 indicates that PU/PUE is present. We use an energy detection based in [45,46]; N is the total number of samples of the energy, and Y(n) are added during one S i detection interval, as shown in Eq. 11.…”

Section: Y ðNþ ¼mentioning

confidence: 99%

“…where Q(.) is the Gaussian complementary function [46]. Table 2 shows the simulation parameters for energy detector.…”

Section: Y ðNþ ¼mentioning

confidence: 99%

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Detection of dynamic location primary user emulation on mobile cognitive radio networks using USRP

Muñoz

Rodríguez-Colina

Martínez

et al. 2020

J Wireless Com Network

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The study and the detection of possible network attacks are essential for wireless networks, in particular for mobile cognitive radio networks due to its characteristics such as the dynamic spectrum allocation and constant frequency hopping. The primary user emulation attack is one of the most significant attacks in cognitive radio, because it hazards the complete cognitive cycle. The techniques used for the detection of primary user emulation found in the literature are based on a fixed attacker location. However, in a mobile environment, the attacker usually has dynamic locations and this compromises the current applied security techniques and generates inefficient attack detection. Therefore, our work proposes a novel technique using cross-layer design for the detection of primary user emulation with mobility. This attack detection technique was tested with experiments using software-defined radio equipment and mobile phones at indoor scenarios with dynamic locations and with a mobile phone base station built up also with software-defined radio. The obtained results show that the combination of the three utilized techniques, energy detection, motion estimation, and application information analysis, are able to optimize the detection with around 100% of effectiveness for the primary user emulation attack with dynamic location. The proposed technique shows that the energy detection time is around 100 ms and for the processing time of the information analysis in the mobile phone is about 30 s. This result shows a practical and effective approach to detect primary emulation attacks. The proposed technique, to the best of the authors' knowledge, has not been presented before in the literature with experiments neither with mobility conditions of the attacker as presented in our proposed work.

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Cooperative Energy Spectrum Sensing for Mobile Cognitive Radio Networks using SDR

Munoz

Ernesto

Martinez

et al. 2020

2020 IEEE Colombian Conference on Communications and Computing (COLCOM)

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Sensing-Throughput tradeoff with primary user traffic and cooperative sensing in cognitive radio

Cited by 5 publications

References 8 publications

Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

Detection of dynamic location primary user emulation on mobile cognitive radio networks using USRP

Cooperative Energy Spectrum Sensing for Mobile Cognitive Radio Networks using SDR

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