Defense against PUEA and SSDF attacks in cognitive radio networks

Chaitanya, D. L.; Chari, K. Manjunatha

doi:10.1109/get.2016.7916832

Cited by 5 publications

(2 citation statements)

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“…Average number of secondary users left for transmitting their sensing result is given by equation 13and transmission probability success is given by equations (14) and (15)…”

Section: Calculation Of Energy Sensing Model Using Dynamic Spectrum Smentioning

confidence: 99%

“…Such an attack can only be prevented by having better cross-layer design. 14 Hard decision fusion rules 15 are proposed for the prevention of PUE attack such that the fusion centre based on the result of the fusion of all these rules determines the actual primary user, thus preventing the attack. The key point is to find the collisions to the primary user that will not happen if all the secondary users obey the fusion centre rules.…”

Section: Introductionmentioning

confidence: 99%

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Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks

Srinivasan

Shivakumar²,

Muazzam

2019

International Journal of Distributed Sensor Networks

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Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

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“…Average number of secondary users left for transmitting their sensing result is given by equation 13and transmission probability success is given by equations (14) and (15)…”

Section: Calculation Of Energy Sensing Model Using Dynamic Spectrum Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%