9281

8583

كارشناسي ارشد

برق و كامپيوتر-مخابرات﴿شبكه﴾

اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر

1393

نه،63ص.: مصور،جدول،نمودار

ص.ع.به فارسي و انگليسي

مهدي مهدوي

5/9/93

علي محمد دوست حسيني

مهندسي برق و كامپيوتر

ID8583

Using LOF and SLOF for Trust User Selection in Cooperation Spectrum Sensing with the Presence of Malicious Users in Cognitive Radio Networks Fatemeh Amini f amini@ec iut ac ir Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Degree M Sc Languge FarsiSupervisor Mehdi Mahdavi m mahdavi@cc iut ac irAbstract Cognitive Radio CR technology has been proposed in order to make efficient usage of spectrum In thistechnology users who have no spectrum licenses also known as secondary users SUs are allowed to usetemporarily unused licensed spectrum CR is based on effective spectrum sensing Some environmentalconsequences such as multi path fading shadowing and hidden terminals affect the results of spectrumsensing obtained by CR users Cooperative Spectrum Sensing CSS is suggested to decrease suchconsequences In CSS CR users share their information obtained during individual spectrum sensing andmake cooperative decisions which is more accurate than individual decisions The cooperation among CRusers raises new concerns on the reliability and the security in cooperative sensing This is because whenmultiple CR users cooperate in sensing it is possible some CR users which are malicious users MUs report unreliable or falsified sensing data which can easily influence the cooperative decision Among themethods presented to detect and eliminate the effect of MUs outlier based method can be pointed Thesensible definition of an outlier is an observation that highly deviates from other observations makessuspicion that such an observation has been generated by a different mechanism In this thesis we the LocalOutlier Factor LOF and Simple Local Outlier Factor SLOF have been used for eliminating the MUsData In this thesis to defense against MUs cooperation schemes have been presented In such schemes energy detection and cyclostationary feature detection have been implemented Then in order to remove theMUs data LOF and SLOF values are allocated to each of sensed data and those data whose LOF and SLOFare greater than a threshold value are identified as MUs data and removed from combination process It isshown that identifying MUs based on the proposed schemes does not depend on detection method Furthermore the proposed schemes do not require any other pre knowledge about data distribution primarynetwork location of primary transmitter and location of secondary users In other words the proposedschemes do not require to keep the history of CR users behaviors and are able to detect and eliminateseveral MU Keywords Cognitive radio Networks cooperative spectrum sensing malicious user detection incooperative spectrum sensing

مهدي مهدوي

علي محمد دوست حسيني