Primary user emulation and jamming attack detection in cognitive radio via sparse coding

Furqan, Haji M.; Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin

doi:10.1186/s13638-020-01736-y

Cited by 23 publications

(14 citation statements)

References 34 publications

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“…The authors in [92] investigated the joint detection of PUE and jamming attacks in CRN. A sparse coding of the compressed received signal based detection algorithm was proposed.…”

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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“…The authors in [92] investigated the joint detection of PUE and jamming attacks in CRN. A sparse coding of the compressed received signal based detection algorithm was proposed.…”

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.

View full text Add to dashboard Cite

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“…In [13], the authors devised a primary user emulation detection technique for jamming attempts in cognitive radio. The suggested approach is based on the compressed signal coding in a dictionary which depends on the channel.…”

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Networks for Secure Data Transmission in Wireless Network

Jayabalan¹,

Pugazendi²

2023

Intelligent Automation &Amp; Soft Computing

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In this paper, a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios. It is designed further to make their transmission decision that automatically adapts to the transmission dynamics to mitigate the launched jamming attacks. The generative adversarial learning neural network (GALNN) or generative dynamic neural network (GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompass minimax game theory. The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an increased detection rate in the generative adversarial network (GAN). The GDNN with game theory is designed to validate the channel condition with the cross entropy loss function and back-propagation algorithm, which improves the communication reliability in the network. The simulation is conducted in NS2.34 tool against several performance metrics to reduce the misdetection rate and false alarm rates. The results show that the GDNN obtains an increased rate of successful transmission by taking optimal actions to act as a defense mechanism to mislead the jammer, where the jammer makes high misclassification errors on transmission dynamics.

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“…Research on detecting PUE attacks is an ongoing activity. A lot of work in this area has been reported in the literature [1] - [17]. In [1], an analytical model and a practical mechanism to detect PUEA in CRNs (a.k.a.…”

Section: Related Workmentioning

confidence: 99%

“…Device-specific features have been employed to propose a passive, non-parametric classification method to determine the number of PUs that are transmitting in the spectrum. In [17], an algorithm for the detection of PUEA and jamming attacks in CRN is proposed, which is founded on the sparse coding of the compressed received signal over a channel-dependent dictionary.…”

Section: Related Workmentioning

confidence: 99%

Detection Of Primary User Emulation Attack (PUEA) In Cognitive Radio Networks Using One-Class Classification

Chhetry,

Marchang

2021

Preprint

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Opportunistic usage of spectrum owned by licensed (or primary) users is the cornerstone on which the Cognitive Radio technology is built. Unlicensed (or secondary) users that thus use the spectrum rely opportunistically on spectrum sensing to determine the presence of primary user signal. In such a context, an attacker may mimic the behavior of a primary user (PU) to deceive the secondary users (SUs) into believing that a PU signal is present whereas it is not. Such an attack is known as the Primary User Emulation Attack (PUEA). A malicious user may launch a PUEA with the intention of grabbing the vacant bands for its own transmission. Another reason may be to simply disrupt the functioning of the Cognitive Radio Network (CRN). This work investigates the use of one-class classification for detecting PUEA in an infrastructure-based CRN. We opine that sensing data collected at the fusion center mainly for Collaborative Spectrum Sensing (CSS) can be exploited to characterize a PU signal. The PU signal features thus learned can aid in distinguishing a PU signal from a PU signal emulation. In particular, we investigate the use of one-class classification techniques, viz., Isolation Forest, Support Vector Machines (SVM), Minimum Covariance Determinant(MCD) and Local Outlier Factor(LOF) for detection of PUEA attacks. Simulation results support the validity of using one-class classification for detection of PUEA.

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Primary user emulation and jamming attack detection in cognitive radio via sparse coding

Cited by 23 publications

References 34 publications

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Generative Adversarial Networks for Secure Data Transmission in Wireless Network

Detection Of Primary User Emulation Attack (PUEA) In Cognitive Radio Networks Using One-Class Classification

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