On Ensemble Learning-Based Secure Fusion Strategy for Robust Cooperative Sensing in Full-Duplex Cognitive Radio Networks

Zhang, Yirun; Wu, Qirui; Shikh-Bahaei, Mohammad

doi:10.1109/tcomm.2020.3005708

Cited by 34 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To defend against various malicious attacks and interference in full-duplex CRNs (FD-CRNs), an ensemble ML (EML) based robust CSS framework was proposed in [110], [111]. SUs were assumed to have the ability to sense and transmit over the same frequency band simultaneously.…”

Section: ) Ssdf and Pue Combination Attacksmentioning

confidence: 99%

“…Studying the characteristics of different ML solutions and designing a combination SS solution more in line with the characteristics and requirements of the system could further improve performance. For example, the ensemble learning used in the literature [110], [111] improved the efficiency of the system by combining linear regression, neural networks, and other solutions, allowing it to combat PUE and SSDF quickly and efficiently. There is a real need for research on new protocols and frameworks for such coexistence.…”

Section: A Cross Technology Coexistencementioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract

Section: ) Ssdf and Pue Combination Attacksmentioning

confidence: 99%

Section: A Cross Technology Coexistencementioning

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

show abstract

“…transmission state prediction, feedback collection) are transmitted and stored in a public manner, which can easily be misuse by the attacker, leading to poor security. Ensemble learning based detection of the presence of malicious secondary users in a cognitive network was proposed in [24]. The degradation in the performance of sensing caused by malicious users in the network was addressed in this approach.…”

Section: A Schemes For Security Attacks In Crnmentioning

confidence: 99%

BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles

Pari

Jaisankar

2022

IEEE Access

View full text Add to dashboard Cite

Cognitive Radio (CR) is a wireless communication system that is used for intelligent vehicles to solve spectrum scarcity and improve the utilization of the spectrum. However, spectrum sensing and data sharing are difficult due to the presence of malicious nodes which degrades the performance. To overcome these issues, we proposed the BlockCRN-IoCV method which includes authentication, density aware clustering, dual agent based spectrum access and secure beamforming. Here, authentication is performed for both Primary Users (PUs) and Secondary Users (SUs) using the Hybrid Advanced Encryption Standard and Hyper-elliptic Curve Cryptography (AES-HCC) algorithm by considering ID, PUF and location which ensures the legitimacy of the users. To address the mobility of the vehicle we perform density aware clustering using Density aware Dynamic Radius Clustering (DADRC) by considering location, distance and direction for increasing throughput. After completing clustering, we perform efficient spectrum access by using the Dual Agent based Twin Delayed (DA-TD3) algorithm which includes two agents, the first agent performs spectrum sensing by considering SNR, noise level and trust, and the second agent performs spectrum allocation by considering Channel State Information (CSI), in which the CSI is predicted by Quasi-Newton Iterative Unscented Kalman Filter (QNIUKF) algorithm for effective data transmission. Finally, secure beamforming is performed using Bi-Gated Recurrent Neural Network (BiGRU-CapsNet) by considering CSI, beam score, array factor, and direction of angle. The simulation is carried out by OMNET++ and SUMO simulation tools and the performance of this work is evaluated by throughput, packet delivery ratio, SNR, detection accuracy, BER, and delay. The simulation result shows that the proposed work achieves superior performance compared to existing work for secure spectrum sensing and beamforming.

show abstract

“…Incorporating various techniques, such as sensing algorithms [3][4][5][6][7], access strategies, fusion rules, and user cooperation, a number of works [16,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] have focused on the performance improvements of CRNs with CSS. From the perspective of access strategy, Lee et al [20] proposed an adaptive CSS scheme using random access.…”

Section: Introductionmentioning

confidence: 99%

“…Golvaei et al [28] proposed a soft decision algorithm to improve CSS performance for hidden PUs in fading and shadowing environments, which showed better performance than the hard decision algorithm. Yuan et al [29] and Zhang et al [30], respectively, proposed a secure fusion strategy to defend against malicious users for CSS. Taking the location impact of different SUs into account, Liu et al [31] proposed a probability-based fusion rule.…”

Section: Introductionmentioning

confidence: 99%

Trade-Offs among Sensing, Reporting, and Transmission in Cooperative CRNs

Liu

Zheng

2022

Sensors

View full text Add to dashboard Cite

Cooperative spectrum sensing (CSS) has been verified as an effective approach to improve the sensing performances of cognitive radio networks (CRNs). Compared with existing works that commonly consider fusion with fixed inputs and neglect the duration of the reporting period in the design, we novelly investigate a fundamental trade-off among three periods of CSS: sensing, reporting, and transmission periods, and evaluate the impact of the fusion rule with a varying number of local sensing results. To be specific, the sensing time could be traded for additional mini-slots to report more local sensing results for fusion, or it could be traded for longer transmission time. In the CRNs with a given durations of sensing/reporting/transmission periods, we, respectively, formulate the throughput and collision probability and optimize the throughput under the collision constraint. The theoretical results show that, in the specific value intervals of the sensing parameters, the collision constraint provides an upper bound of the number of mini-slots in the reporting period or a lower bound of the sensing duration. We provide the approach to the maximum throughput in some cases.Finally, numerical results are presented to validate theoretical results.

show abstract

On Ensemble Learning-Based Secure Fusion Strategy for Robust Cooperative Sensing in Full-Duplex Cognitive Radio Networks

Cited by 34 publications

References 29 publications

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles

Trade-Offs among Sensing, Reporting, and Transmission in Cooperative CRNs

Contact Info

Product

Resources

About