Effective Solutions for Cross Layer Attacks in Cognitive Radio Network

Language: English

Published by LAP LAMBERT Academic Publishing Apr 2022, 2022

6204748750 / 9786204748757

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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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This item is printed on demand - it takes 3-4 days longer - Neuware -Cognitive Radio Networks (CRNs) have come out as an encouraging next-generation network technology that addresses the issues related to dynamic Spectrum Access and enhanced utilization of spectrum in a significant manner. Specifically Trust, Reputation Management models and Cross layer defense mechanism are more and more regarded for CRNs to secure them against the attacks posed by the secondary users. In this Work, a method called, Mean Bid Trust & Multiple Nash Reputation (MBT-MNR) and Optimized Levensthein Cross layer Defense framework methods are proposed to secure the CRN by detecting the attackers at two different layers, Physical and Data link layers. Mean Bid Cross Layer Trust Evaluation model is applied to measure the trustworthiness of secondary user by third party. Followed by which, the classification of malicious and normal user is made by applying the Multiple Nash Game Theory model. Optimized Levesthein Nearest Centroid Framework (OS-LNCC) is proposed to mitigate Cross Layer attacks in CRN's. The performance of both the methods is evaluated by various parameters such as energy consumption, detection time, Sensing Delay, Throughput and detection accuracy. 96 pp. Englisch.

Seller Inventory # 9786204748757

Title
Effective Solutions for Cross Layer Attacks in Cognitive Radio Network
Author
Ganesh Davanam
Publisher
LAP LAMBERT Academic Publishing Apr 2022
Publication year
2022
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6204748750
ISBN 13
9786204748757
Item weight
161 grams
Dimensions
220x150x6 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

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BuchWeltWeit Ludwig Meier e.K.

Germany