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

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1282
Author(s):  
Ernesto Cadena Muñoz ◽  
Luis Fernando Pedraza Martínez ◽  
Jorge Eduardo Ortiz Triviño
Keyword(s):  
Support Vector Machine ◽  
Cognitive Radio ◽  
Software Defined Radio ◽  
Denial Of Service ◽  
Primary User ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Secondary Users ◽  
Low Snr ◽  
Primary User Emulation

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.

scholarly journals Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Sajjad Khan ◽  
Liaqat Khan ◽  
Noor Gul ◽  
Muhammad Amir ◽  
Junsu Kim ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Learning Algorithm ◽  
Evidence Theory ◽  
Primary User ◽  
Support Vector ◽  
Radio Network ◽  
Secondary Users

Cognitive radio is an intelligent radio network that has advancement over traditional radio. The difference between the traditional radio and the cognitive radio is that all the unused frequency spectrum can be utilized to the best of available resources in the cognitive radio unlike the traditional radio. The core technology of cognitive radio is spectrum sensing, in which secondary users (SUs) opportunistically access the spectrum while avoiding interference to primary user (PU) channels. Various aspects of the spectrum sensing have been studied from the perspective of cognitive radio. Cooperative spectrum sensing (CSS) technique provides a promising performance, compared with individual sensing techniques. However, the existence of malicious users (MUs) highly degrades the performance of cognitive radio network (CRN) by sending falsified results to a fusion center (FC). In this paper, we propose a machine learning algorithm based on support vector machine (SVM) to classify legitimate SUs and MUs in the CRN. The proposed SVM-based algorithm is used for both classification and regression. It clearly classifies legitimate SUs and MUs by drawing a hyperplane on the base of maximal margin. After successful classification, the sensing results from the legitimate SUs are combined at the FC by utilizing Dempster-Shafer (DS) evidence theory. The effectiveness of the proposed SVM-based classification algorithm is demonstrated through simulations, compared with existing schemes.

Detecting PUE Attack by Measuring Aberrational Node Behavior in CWSN

2018 ◽  
Vol 18 (01) ◽  
pp. 1850004
Author(s):  
PINAKI SANKAR CHATTERJEE ◽  
MONIDEEPA ROY
Keyword(s):  
Denial Of Service ◽  
Primary User ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Dos Attack ◽  
Secondary Users ◽  
Primary User Emulation ◽  
Normal Behavior ◽  
Behavior Profile ◽  
Cognitive Wireless Sensor Network

Primary User Emulation (PUE) attack is a type of Denial of Service (DoS) attack in Cognitive Wireless Sensor Network (CWSN), where malicious secondary users (SU) try to emulate primary users (PU) to maximize their own spectrum usage or obstruct other SU from accessing the spectrum. In this paper, we have designed an application to monitor the SU’s behavior with respect to the CWSN normal behavior profile towards it’s one hop neighbor. Abnormal behavior towards PUE attack of any SU helps us to identify PUE attackers in the network. Our application does not require extensive computational capabilities and memory and therefore suitable for resource constraint cognitive sensor nodes.

scholarly journals Spectrum Sensing to Detect N-Number of Primary Users Using N-Number Secondary Users by Applying Support Vector Machine.

2021 ◽  
Author(s):  
venkateshkumar Udayamoorthy ◽  
Ramakrishnan Sriniva
Keyword(s):  
Support Vector Machine ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Support Vector ◽  
Secondary Users ◽  
Primary Users ◽  
Svm Algorithm ◽  
Rbf Kernel

Abstract In this paper, a cooperative spectrum sensing (CSS) model is proposed to sense n-number of primary users (PUs) using n-number secondary users (SUs) in a sequence by applying support vector machine (SVM) algorithm using three different kernels namely linear, polynomial and radial basis function (RBF) respectively. In this method, fusion centre (FC) instructs all the SUs through control channel, which PU is to be sensed by sending a pre-defined primary user identification code (PUid) and each SU sense the Kth PU spectrum information and stored in a database at FC. SU transmits a bit ‘0’ or bit ‘1’ along with PU sensing information to the FC to indicate whether it needs a spectrum band to transmit the data or not. SU add two identification codes along with sensing information to the FC which indicates that from which SU the sensing information received and which PU is sensed by the SU. For simulation 500 data samples are used and the simulation results show an accuracy of 96% and false alarm value of 1.3% in classifying the SU sensing information at FC using RBF kernel. Another method is proposed with multiclass classification by applying SVM algorithm using RBF kernel. The confusion region class is classified with zero false alarm percentage and achieves an accuracy of 99.3% in classifying the SU sensing information at FC.

A Spectrum Sensing Based on Support Vector Machine Algorithm in the Building Indoors Environment

2014 ◽  
Vol 945-949 ◽  
pp. 2297-2300 ◽  
Author(s):  
Xing Hua Xia ◽  
Fang Jun Luan ◽  
Meng Xin Li
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Spectrum Sensing ◽  
Indoor Environment ◽  
Signal To Noise Ratio ◽  
Primary User ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Spectral Correlation ◽  
Low Snr

Spectrum sensing performance of building indoor environment has been the focus of attention and research in low signal-to-noise ratio. In this paper, a primary users sensing approach to signal classification combining spectral correlation analysis and support vector machine (SVM) is introduced. Three spectral coherence characteristic parameters are chosen via spectral correlation analysis. By utilizing a nonlinear SVM, primary user signal has been detected. Simulations indicate that the overall success rate is above 90.2% when SNR is equal to-5dB and 80.1% in-15dB. Compared to the existing methods including the classifiers based on MME and ANN, the proposed approach is more effective in the case of low SNR and limited training numbers. The results show that the validity and superiority of the proposed algorithm in building indoor environment.

scholarly journals Primary user emulation characterization in mobile cognitive radio networks

2020 ◽  
Vol 14 (1) ◽  
pp. 26-34
Author(s):  
Ernesto Cadena-Muñoz ◽  
Luis Fernando Pedraza-Martínez ◽  
Enrique Rodríguez-Colina
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Software Defined Radio ◽  
Power Level ◽  
Primary User ◽  
Radio Networks ◽  
Cognitive Network ◽  
Primary User Emulation ◽  
Primary Network ◽  
Radio Equipment

This paper presents the results of the characterization of the attack known as the "primary user emulation" in mobile cognitive radio networks performing the implementation and testing. The tools and their configuration to carry out the attack are presented and their effects on the network are analyzed. The results show how to generate the attack with a software-defined radio equipment (SDR) using GNU-Radio and OpenBTS. The effects of the possible configurations of the attack on the network are shown, the malicious type generates constant interference on the primary or cognitive network, the selfish type allows to imitate a licensed or primary user generating interference to the primary network and inability to access the Cognitive Network while active. If the emulator's power level is fixed, the services it provides are stable. If the power is variable the services suffer intermittency. Primary user emulation is the attack that most affects the cognitive radio network so its effects are analyzed in order to propose ways of detecting or applying countermeasures.

Cognitive Radio: Primary User Emulation Attacks and Remedies

2012 ◽  
Vol 5 (2) ◽  
pp. 103-108 ◽  
Author(s):  
Sachin Shetty ◽  
Meena Thanu ◽  
Ravi Ramachandran
Keyword(s):  
Cognitive Radio ◽  
Primary User ◽  
Primary User Emulation

Throughput performance under primary user emulation attack in cognitive radio networks

2017 ◽  
Vol 30 (18) ◽  
pp. e3371 ◽  
Author(s):  
Kuldeep Yadav ◽  
Binod Prasad ◽  
Abhijit Bhowmick ◽  
Sanjay Dhar Roy ◽  
Sumit Kundu
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Throughput Performance ◽  
Primary User Emulation Attack ◽  
Primary User Emulation
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