Security Issues in Cognitive Radio Networks

Mathur, Chetan Nanjunda; Subbalakshmi, K. P.

doi:10.1002/9780470515143.ch11

Cited by 37 publications

(17 citation statements)

References 32 publications

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“…Within a cooperative framework where secondary users exchange sensing information, false feedback from one or a group of malicious users could lead the CRN to take improper actions [23]. For example, the CRN could conclude that a given frequency band is occupied by a primary user when actually it is not the case or, the other way round, it could consider it as a vacant band when being used by a primary network.…”

Section: False Feedbackmentioning

confidence: 97%

“…As a consequence data must be secured by means of cryptographic primitives. [18] selfish [17] Jellyfish [19] OFA [20] selective forwarding [17] Lion [21] CCDA [22] false feedback [17,23] worm/sink -hole [17] • Modification of data: An attacker could try to modify the data exchanged between several entities to its own advantage. Thus, integrity of data must be assured.…”

Section: Attacks To Cognitive Radio Networkmentioning

confidence: 99%

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Securing cognitive radio networks

LeónO.

Hernández-Serrano

SorianoM.

2010

Int J Communication

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Section: False Feedbackmentioning

confidence: 97%

Section: Attacks To Cognitive Radio Networkmentioning

confidence: 99%

Securing cognitive radio networks

LeónO.

Hernández-Serrano

SorianoM.

2010

Int J Communication

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“…Frank Fitzek (Aalborg University) and Marcos Katz (Samsung) notably bring together ideas for CWN CRA characterized by cooperation among intelligent entities [3]. Cooperation results from not just game theoretic considerations [60] but also from considerations of power optimization [61,62]; relays [63] and ad hoc discovery and routing [64]; diversity [65]; cross-layer optimization [66]; stability and security [67,68]; and spectrum efficiency considerations [53]. Distributed antennas [69,70], cooperative header compression [71] and coding [72], and distributed spatial channel control [73], among others, result from such a focus on cooperation in cognitive wireless networks.…”

Section: Networking and Cra Evolutionmentioning

confidence: 98%

Cognitive radio architecture evolution: annals of telecommunications

Mitola

2009

Ann. Telecommun.

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Cognitive radio research has developed dynamic radio spectrum management to enhance spectrum efficiency, e.g., as secondary users in unused TV bands. The location and user context of the mobile wireless user that regulatory bodies and lawmakers view as significant to spectrum interference policies have not been addressed as thoroughly.In addition, quality of service (QoS) provides a starting point but does not guarantee quality of experience (QoE) that depends on quality of information (QoI) which is a function of place, time, and user state in a social setting (e.g., commuting, shopping, or in need of medical assistance). This paper considers the evolution of cognitive radio architecture (CRA) from dynamic spectrum access (DSA) to QoE via an interdisciplinary perspective. Machine perception in visual, acoustic, speech, and text domains can cue the automatic detection of user state in stereotypical situations, enabling cognitive nodes and networks to select from among radio bands and modes more appropriately, thus enabling cognitive wireless networks (CWNs) to deliver higher QoE within technical policy constraints, in a way that makes cost-effective use of embedded and distributed computational intelligence. The control of networks of such cognitive radios requires advances in policy language architectures, so this paper introduces cognitive linguistics for policy languages.

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“…Among these, the energy based detection is more popular and easier to implement. T International Conference on Artificial Intelligence, Energy and Manufacturing Engineering (ICAEME'2015) Jan. [7][8]2015…”

Section: Introductionmentioning

confidence: 99%

Physical Layer Security in Cognitive Radio Networks: Detection and Mitigation of Primary User Emulation Attacks

Ammar¹,

Riley²,

Mehdawi³

et al. 2015

International Conference on Artificial Intelligence, Energy and Manufacturing Engineering (ICAEME'2015) Jan. 7-8, 2015 Dubai (U

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Cognitive Radio Networks (CR) is an advanced growing technique and a promising technology for the upcoming generation of the wireless networks in order to efficiently utilize the limited spectrum resources and satisfy the rapidly increasing demand for wireless applications and services. Deployment of such networks is hindered by the vulnerabilities that these networks are exposed to, in this paper we focus on security problems arising from Primary User Emulation Attacks (PUEA) in CR networks. We present a comprehensive introduction to primary user emulation attacks, from the attacking rationale and its impact on CR networks, to detection and defense approaches. We have setup the system model using Matlab software, we have used the Neyman-Pearson composite hypothesis test NPCHT to obtain the hypothesis test and detect the PUEA. In order to secure CR networks against PUE attacks, we considered the power received at the secondary receiver.Simulation results proved that using the NPCHT it is possible to keep the probability of success of PUEA low depends on the threshold value. The number of malicious users in the system can significantly increase the probability of false alarm in the network, Also it shows that there is a range of network radii in which PUEA are most successful.

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Security Issues in Cognitive Radio Networks

Cited by 37 publications

References 32 publications

Securing cognitive radio networks

Securing cognitive radio networks

Cognitive radio architecture evolution: annals of telecommunications

Physical Layer Security in Cognitive Radio Networks: Detection and Mitigation of Primary User Emulation Attacks

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