Cooperative Spectrum Sensing with Entropy for Mobile Cognitive Radio Networks

Ernesto, Cadena Munoz; Martínez, Julián Andrés Pacheco; Martínez, Luis Fernando Pedraza; Parra, Ingrid Patricia Paez

doi:10.1109/andescon50619.2020.9272096

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…A possible solution for some of these problems is to share the information of all the users of the CRN; this means sharing the spectrum sensing data, the detected signal, frequency, and bands of use since these data increase detection performance, providing a solution that can be implemented for an MCRN [6]. We implemented this solution only for spectrum sensing and not for PUE detection [8].…”

Section: Previous Workmentioning

confidence: 99%

“…The results of the entropy calculation on each SU of the CSS using Equation ( 4) are used as an input for the DNN algorithm in the learning and detecting processes; the binary results of the detection are sent to the FC as we worked it out in [8]. For a given number of bins X, using the histogram method, the statistic test is calculated in Equation (4).…”

Section: Detector Based On Entropymentioning

confidence: 99%

“…where N is the number of samples and λ is the entropy threshold calculated with the entropy H X for X bins and described in Equation ( 5). The probability of a false alarm is P FA and σ e is the standard deviation [8].…”

Section: Detector Based On Entropymentioning

confidence: 99%

“…In our previous work, we used entropy detection for spectrum sensing [7], and in [8], we explored the CSS for spectrum sensing but not for detecting the PUE attack. In [9] and [10], we use the SVM and KNN to detect PUE attacks with a PUE detector implemented in a single SU.…”

Section: Introductionmentioning

confidence: 99%

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PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

Muñoz,

Chica Pedraza,

Cubillos-Sánchez

et al. 2023

Future Internet

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The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the PUE attack and recording the data for future reference by the attacker, sharing the data for all the SU.

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Section: Previous Workmentioning

confidence: 99%

Section: Detector Based On Entropymentioning

confidence: 99%

Section: Detector Based On Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

Muñoz,

Chica Pedraza,

Cubillos-Sánchez

et al. 2023

Future Internet

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“…For example, the authors in [ 17 ] have applied Shannon entropy in a cooperative spectrum sensing design obtaining a higher detection performance than the ED. In another recent study [ 18 ], the authors have applied Shannon entropy measures to a cooperative spectrum sensing implementation using USRP equipment. The results confirm the superior detection performance on the application of the entropy measure in comparison to the ED.…”

Section: Related Workmentioning

confidence: 99%

Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

Baldini

Chareau

Bonavitacola

2021

Entropy

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Spectrum sensing is an important function in radio frequency spectrum management and cognitive radio networks. Spectrum sensing is used by one wireless system (e.g., a secondary user) to detect the presence of a wireless service with higher priority (e.g., a primary user) with which it has to coexist in the radio frequency spectrum. If the wireless signal is detected, the second user system releases the given frequency to maintain the principle of not interfering. This paper proposes a machine learning implementation of spectrum sensing using the entropy measure as a feature vector. In the training phase, the information about the activity of the wireless service with higher priority is gathered, and the model is formed. In the classification phase, the wireless system compares the current sensing report to the created model to calculate the posterior probability and classify the sensing report into either the presence or absence of wireless service with higher priority. This paper proposes the novel application of the Fluctuation Dispersion Entropy (FDE) measure recently introduced in the research community as a feature vector to build the model and implement the classification. An improved implementation of the FDE (IFDE) is used to enhance the robustness to noise. IFDE is further enhanced with an adaptive method (AIFDE) to automatically select the hyper-parameter introduced in IFDE. Then, this paper combines the machine learning approach with the entropy measure approach, which are both recent developments in spectrum sensing research. The approach is compared to similar approaches in literature and the classical energy detection method using a generated radar signal data set with different conditions of SNR(dB) and fading conditions. The results show that the proposed approach is able to outperform the approaches from literature based on other entropy measures or the Energy Detector (ED) in a consistent way across different levels of SNR and fading conditions.

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Cooperative Spectrum Sensing with Entropy for Mobile Cognitive Radio Networks

Cited by 2 publications

References 12 publications

PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

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