On physical layer security for cognitive radio networks

Shu, Zhihui; Qian, Yi; Ci, Song

doi:10.1109/mnet.2013.6523805

Cited by 155 publications

(9 citation statements)

References 10 publications

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“…This can be implemented by letting S to share a j with L (e.g., the seed of the jamming signal generator at S is shared with L in a secure manner through a cooperation hand-shaking solely among S and L before information transmission starts). Such a jamming signal generation is widely accepted in most existing works (e.g., [34][35][36][37][38][39][40][41][42][43]). Accordingly, the legal receiver L can exactly re-generate the jamming signal and completely take it out of its received signal, intimately obtaining the jamming-free signal at L aŝ…”

Section: System Modelmentioning

confidence: 99%

Security Capability Analysis of Cognitive Radio Network With Secondary User Capable of Jamming and Self-Powering

Dan¹,

Van²,

Ngoc³

et al. 2020

JCC

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This paper investigates a cognitive radio network where a secondary sender assists a primarytransmitter in relaying primary information to a primary receiver and also transmits its own information toa secondary recipient. This sender is capable of jamming to protect secondary and/or primary informationagainst an eavesdropper and self-powering by harvesting radio frequency energy of primary signals.Security capability of both secondary and primary networks are analyzed in terms of secrecy outageprobability. Numerous results corroborate the proposed analysis which serves as a design guidelineto quickly assess and optimize security performance. More importantly, security capability trade-offbetween secondary and primary networks can be totally controlled with appropriate selection of systemparameters.

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Section: System Modelmentioning

confidence: 99%

Security Capability Analysis of Cognitive Radio Network With Secondary User Capable of Jamming and Self-Powering

Dan¹,

Van²,

Ngoc³

et al. 2020

JCC

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“…To ensure security, physical-layer security technology is an effective confidentially protection mechanism [18,19,20,21,22]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [22] emphasized that both the primary users (PUs) and secondary users (SUs) must be defended from eavesdropping in cognitive networks. Specifically, it is legitimate that the SUs are allowed to access the primary spectrum by cooperating with the PUs, where the SUs act as a relay or a friendly jammer to elevate the PU’s secrecy [24].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Jammer Selection for Secrecy Improvement in Cognitive Internet of Things

Xie

Xing

et al. 2018

Sensors

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Smart homes can improve the quality of life and be implemented by Internet of Things (IoT) technologies. However, security is a very important issue in smart homes. For this reason, we propose a secrecy transmission protocol for primary user (PU) by selecting friendly jammer in cognitive IoT model. In particular, a secondary transmitter (ST) is selected to transmit secondary signals by the PU’s frequency spectrum, while another ST is chosen to transmit artificial noise to protect the transmission confidentiality of the PU against eavesdropping. Moreover, two selection schemes are presented to confirm the former and the latter ST, and the goal is to optimize the secondary transmission performance and the primary security performance, respectively. For the non-security model and the proposed protocol, we derive the closed-form expressions of the intercept probability and the outage probability for the PU. We also obtain the closed-form expression of outage probability for the secondary user. The numerical results show that the security performance of the PU is significantly enhanced in our protocol compared to the non-security model. In addition, the outage performance of the secondary users is also improved in high secondary transmit SNR region.

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“…On the other hand, securing the communication between legitimate SUs is a challenging issue due to the fact that numerous attacks can be launched against cognitive radio networks. Comprehensive studies on this aspect [3,4,5] show that two of the major physical layer attacks against cognitive radio networks are spectrum sensing data falsification (SSDF) and eavesdropping. SSDF is performed on a collaborative sensing setup [6]: an attacker sends false spectrum sensing data to other SUs, in case of distributed sensing decision, or to the fusion center [7], resulting in a wrong spectrum access decision.…”

Section: Introductionmentioning

confidence: 99%

Exploiting spectrum sensing data for key management

Badawy

Elfouly

Chiasserini

et al. 2017

Computer Communications

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In cognitive radio networks, secondary users (SUs) communicate on unused spectrum slots in the frequency bands assigned to primary users (PUs). Like any other wireless communication system, cognitive radio networks are exposed to physical layer attacks. In particular, we focus on two common attacks, namely, spectrum sensing data falsification and eavesdropping. Such attacks can be counteracted by using symmetric key algorithms, which however require a complex key management scheme. In this paper we propose a novel algorithm that significantly reduces the complexity of the management of symmetric link keys by leveraging spectrum sensing data that is available to all nodes. In our algorithm, it is assumed that a primary secret key is pre-distributed to the legitimate SUs, which is needed every number of detection cycles. With the aid of the information provided in the primary key, our algorithm manipulates the collected samples so that a segment of the estimated sensing statistic at the two legitimate SUs can be used as a seed to generate a common symmetric link key. The link key is then employed to encrypt the transmitted data. Our algorithm exhibits very good performance in terms of bit mismatch rate (BMR) between two link keys generated at the two legitimate SUs. In addition, our solution is robust against the difference in the received signal to noise ratio between two legitimate SUs thus making it suitable for practical scenarios. Furthermore, our algorithm exploits the decision statistic that SUs use for spectrum sensing, hence, it does require neither extra processing nor extra time, allowing the SUs to quickly and securely tab into empty spectrum slots.

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On physical layer security for cognitive radio networks

Cited by 155 publications

References 10 publications

Security Capability Analysis of Cognitive Radio Network With Secondary User Capable of Jamming and Self-Powering

Security Capability Analysis of Cognitive Radio Network With Secondary User Capable of Jamming and Self-Powering

Cooperative Jammer Selection for Secrecy Improvement in Cognitive Internet of Things

Exploiting spectrum sensing data for key management

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