Accurate Primary User Emulation Attack (PUEA) Detection in Cognitive Radio Network using KNN and ANN Classifier

Inamdar, Mohammad Azharuddin; Kumaraswamy, H. V.

doi:10.1109/icoei48184.2020.9143015

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…A k-nearest neighbor (KNN) classifier based detection method was used to classify malicious users to forestall PUE attacks in [89]. KNN was trained with the parameters such as data rate, distance, power, frequency of request.…”

Section: ) ML Based Detection Methodsmentioning

confidence: 99%

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Wang

Sun

et al. 2022

IEEE Open J. Commun. Soc.

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The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

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Section: ) ML Based Detection Methodsmentioning

confidence: 99%

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Wang

Sun

et al. 2022

IEEE Open J. Commun. Soc.

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“…By comparing the proposed method with the existing methods including, K-nearest neighbor (KNN) [35], support vector machine (SVM) [36], Deep convolutional neural network (CNN) [37], bidirectional long short-term memory (BiLSTM), neural network (NN), NN with singular spectrum analysis (SNN), NN with class-specific attention CS-NN and BiLSTM with Chip Optimization (CO-BiLSTM), BiLSTM with Monkey Optimization (MO-BiLSTM), Optimized Active Ensembled NN and KCS 2 TM. KNN, SVM, DCNN, BiLSTM, NN, SNN, CS-NN, CO-BiLSTM, MO-BiLSTM, Optimized Active Ensembled NN, KCS 2 TM…”

Section: Comparative Analysismentioning

confidence: 99%

KCS 2 TM: A key sharing approach and Deep Learning Model for Primary User Emulsion Attack detection in Cognitive Radio Networks

Biradar,

Singh,

Patil

2024

Preprint

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The demand for wireless transmission and existence of the cognitive radio networks (CRN) insist the need for detecting the Primary User Emulsion Attack (PUEA) that authenticates the network as the licensed user for accessing the spectrum in CRN. Such condition creates disruptions in the communications. Hence, in this research, PUEA detection in the CRN is proposed using the deep learning and key sharing approach (KCS2TM model). The key sharing approach enables sharing the secret key between Primary User (PU) and Secondary User (SU) that supports the generation of the communication data, which easily assists in detecting the PUEA for which the ChimSp optimizer enabled Bidirectional Long Short Term Memory (CS2TM) model is proposed. The ChimSp optimizer is designed theoretically and mathematically to train the classifier and ensure the improved detection performances. The model provides increased accuracy, highly reliable, reduce computational cost and time are the supremacy of the model. The performance of the model is analyzed with the existing methods, which shows better accuracy sensitivity and specificity of 85.01%, 87.61% and 82.42% compared to other state of art methods.

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“…There are several problems, such as multipath fading, shadowing, and receiver uncertainty, and in an MCRN, there are also different modulation types, and it is difficult to distinguish between them. 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 algorithm is trained with the data rate, distance, and power and compared with the K-nearest neighbor (KNN) classifier, making a classification process. The trained classifier detects the PUE with high performance; simulation results show better accuracy than conventional PUEA classification techniques, including the KNN [6].…”

Section: Introductionmentioning

confidence: 98%

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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Accurate Primary User Emulation Attack (PUEA) Detection in Cognitive Radio Network using KNN and ANN Classifier

Cited by 8 publications

References 13 publications

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

KCS 2 TM: A key sharing approach and Deep Learning Model for Primary User Emulsion Attack detection in Cognitive Radio Networks

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

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