ISSN XXXX XXXX © 2017 IJESC
Research Article
Volume 7 Issue No.3
Detection of Primary User Emulation (PUE) Attack in Cognitive
Radio Network
Priti Patil1 , Prajakta Patil2 , Mr. K.P.Paradeshi3
P.G. Student 1, 2 , Associate Professor3
Depart ment of Electronics Engineering
P.V.P.I.T Budhgaon, Sangli, India
Abstract:
Lots of new technology has been developed in wireless communication, Cognitive rad io is one of the fastest growing technology in
this field. Cognitive rad io is solution to solve spectrum scarcity problem. In this network, un licensed users share the spectrum of
licensed user using spectrum sensing process. Spectrum sensing is important in transmission. When spectrum sensing process is
disturbed, then entire sensing process is disturbed which is called as primary user emulat ion attack. This attack is solved b y using
localization method that is time -difference-of-arrival(TDOA).
Keywords: Cognit ive Rad io (CR), Primary User (PU), Secondary User (SU), Spectru m Hole, Primary User Emulat ion Attack
(PUEA ), Time-Difference-of-Arrival(TDOA.)
I. INTRODUCTION
One of the fastest growing sectors in recent years has
undoubtedly been that of wireless sensor networks (WSNs).
Most wireless sensor network operate on unlicensed frequency
bands. Generally, they use industrial, scientific, and medical
(ISM ) bands, like the worldwide available 2.4-GHz band. This
band is also used in large amount of popular wireless
applications, for examp le, those working over Wi-Fi or
Bluetooth. Hence, the unlicensed spectrum bands are becoming
overcrowded. A CR is an intelligent wireless communication
system, changes its transmission and reception parameter
according to its surrounding environment. And CR also adapts
its internal parameters to achieve reliable and efficient
communicat ions. Cognitive radio network have many
application, such as CR uses TV white spectrum. In cognitive
radio network, there are two types of user primary user (PU) and
secondary user(SU). Primary user is licensed user whereas
secondary user is unlicensed user. SU senses the unused
spectrum that is spectrum hole in opportunistic ways. Spectrum
hole is band of frequencies assigned to primary user, but, at
particular time and geographic location, the band is not being
utilized by that user.
The SUs must have the ability of recognizing the PU fro m SU
signals. Some of the secondary users behave in bad ways and
pretend itself as primary user to access the spectrum. Hence
spectrum sensing process is corrupted is called as primary user
emu lation attack (PUEA). Time-d ifference-of-arrival (TDOA)
method is used to detect position of PUEA attack in cognitive
radio network. There are different types of attack in cognitive
radio namely objective function attack, lion attack, jamming
attack etc. Objective function attack is one type of attack and
goal of OFA is to force CR to select sub optimal value by
changing objective function. Cognitive radio controls rad io
parameter like power, bandwidth, modulation, security and
coding. For examp le, bank transaction does not require high data
rate rather it requires high security during the transmission. For
voice or v ideo application need h igh data rate. Lion attack is one
type of attack in cognitive radio network performed by an
attacker at physical layer. In this attack, attacker performs PUE
attack. PUE allo ws attacker to force secondary users to perform
frequency handoff frequently. Because of frequency handoff is
performed in CRN, co mmunicat ion lin k breaks. Hence degrade
the performance of CRN.
II. PRIMARY US ER EMULATION ATTACK
Figure.1. Representation of s pectrum holes
Secondary users (SUs) sense the unused spectrum using
spectrum sensing process. Spectrum sensing is process to detect
the unutilized parts and used them in opportunistic ways. When
SU is using the unutilized band and detects the existence of
primary user (PU), SU must leave it for him and move to another
free spectrum. The SUs must have the ability of recognizing the
PU fro m SU signals. So me of the secondary users behave in bad
ways and pretend itself as primary user to access the spectrum. It
steals the free spectrum and corrupts the sensing process. Hence
spectrum sensing process is corrupted is called as primary user
emu lation attack (PUEA ).
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Figure 2 represents the actual activity of SU in sensing and
transmission slot. Each secondary user first senses the spectrum
band and if spectrum band is available transmits during
transmission slot and senses again to check for reappearance of
secondary user
composite hypothesis test and wald’s sequential probability rat io
test. In this method, first probability density function is
generated and fro m that result neyman-pearson test is
conducted[7]. Zhao et al [2009], p roposed scheme to counter
PUE attack that is based on transmitter fingerprint ing. In this
scheme, phase noise of noisy career is extracted. Then extracted
part is applied to identify transmitter [9]. Ru iliang Chen et. al
[2008], “Defense against primary user emulation attacks in
cognitive radio networks”, proposed a transmitter verification
scheme, called localizat ion based defense (Loc-Def). This
scheme utilizes both signal characteristics and position of the
signal transmitter to verify primary signal transmitter. Non interactive localization scheme is introduced to detect PUE
attacks [10].
IV. METHODOLOGY
To overcome the PUE attack, a transmitter verification scheme
based on position verification is proposed. Several localization
schemes have been proposed one of them is TDOA. TDOA is
suitable in CRN because it utilize the difference between the
arrival t imes of pulse.
Figure.2. Representation of usual acti vi ty of S U in sensing
and trans mission slot
PUEA has two categories: selfish PUEA and malicious PUEA.
Selfish PUE attacks: Selfish PUE attack is done by a selfish
secondary user. If a selfish node detects the unused spectrum
band, he or she prevents the other secondary users to detect
spectrum so that selfish user could get full access of spectrum.
Malicious PUE attacks: In this attack, attacker does not use
spectrum band for its own co mmun ication. Object ive of attacker
is to reduce the utilization of availab le spectrum band.
III. LITERATURE S URVEY
Ghanem et al [2016], proposed improved primary user emu lation
attack detection technique in based on Firefly Optimization
Algorith m. The main part of this paper is defense against
primary user emulat ion attack (PUEA) in cognitive rad io
network (CRN) based on Time Difference Of Arrival (TDOA)
localization technique depends on proposed firefly optimization
algorith m. And also explained comparison between proposed
algorith m, nonlinear least squares, maximu m likelihood and
Taylor series estimat ion for wireless localizat ion [1]. Fan Jin et.
al [2015], “Improved detection of primary user emu lation attack
in cognitive rad io network”. A new scheme introduced in this
paper. This scheme helps to achieve improved primary user
emu lation (PUE) attack detection in cognitive radio network.
The scheme comb ines energy detection and localization. By
combin ing these two schemes, user can be identified exact
location of primary user emulat ion (PUE) attacker [2]. KaiWang
Lu et. al [2012], “Research of PUE attack based on Location”,
proposed RSSI-based transmitter localizat ion verification
scheme. Th is method calculates the signal transmitter location to
verify the signal is that of a primary signal. This scheme
describes some algorith m namely Maximu m likelihood estimate
algorith m improvement, Coordinate correction technique, robust
maximu m likelihood estimate [5]. Jin et al [2010], p roposed
method to detect PUE attack based on Neyman -Pearson
Localization of TDOA
TDOA is a simple method for positioning. It uses difference of
arrival of time, to find the position of fixed transmitters. To get
the positioning equations, it uses three receiving stations and
uses three dimensional t ime difference o f four stations. The time
difference between the rad iated signal of target and t wo
receiving stations determines a hyperboloid with the two stations
as its two focuses. The four stations produce three pairs of
hyperboloids. A surface of hyperboloids intersecting with
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Figure.3. Block di agram of proposed system
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another surface determines a line, and then the line intersecting
with the third hyperboloid can determine a point, which realizes
positioning
[5]. C. Chen, H. Cheng and Y. Yao(Ju ly,2011), “Cooperative
Spectrum Sensing in Cognitive Rad io Network in the presence
of Primary User Emu lat ion Attack”, IEEE Transactions on
Wireless Co mmunicat ion, Vo l 10, no. 7, pp. 2135-2141.
[6]. D. Pu, Y. Shi, A. V. VIlyashenko and A. M. Wyglinski(Dec
2011), “Detecting Primary User Emu lation attacks in Cognitive
Radio Networks”, In Proceedings of the IEEE Global
Teleco mmunication Conference(GLOBECOM)IEEE, pp. 0105.
[7]. Z. Jin , S. Anand, K.P Subbalakshmi, “Mitigating Primary
User Emu lation Attacks in Dynamic Spectru m Access Networks
using Hypothesis Testing”,in mobile co mputing and
communicat ion review, volu me 13, number 2.
[8]. X. Ding, X. Gong and R. Wu, “The study on related
algorith m of TDOA positioning,” Modern Electronic
Technology, No.1, 2009.
[9]. Caidan Zhao,Wumei Wang, Lianfen Huang and Yan
Yao(September, 2009), “Anti-PUE Attack Based on the
Transmitter Fingerprint Identification in Cognitive Radio”, in
5th International Conference on Wireless Co mmunicat ions,
Networking and Mobile Co mputing (WiCo m‘09), Beijing,
China, pp.1-5.
[10]. R.Chen, J. Park and J. Reed(Jan. 2008), “Defense against
primary user emulation attacks in cognitive radio networks,”
IEEE Journal on Selected Areas in Co mmunications, vol. 26,
no.1, pp. 25-37.
[11]. R Chen and J Park(25-27sept. 2006), “Ensuring
Trustworthy Spectrum Sensing in Cognitive Radio Network”,
1st IEEE workshop on Networking Technologies for Software
Defined Radio, SDR ’06. Pages(s):110-119.
Figure.4. Fl ow chart represents the detecti on of PUE
attacker
V. REFERENC E
[12]. S. Haykin(Feb. 2005), “Cognitive radio : brain-empowered
wireless commun ications,” IEEE Journal on Selected Areas in
Co mmunicat ions, Vo l 23 (2), pp. 201-220. 16
[1] W. R. Ghanem, M. Shokair and M. I. Desouky (Feb 2225,2016), “An imp roved primary user emu lation attack detection
in cognitive radio network based on Firefly Optimization
Algorith m”, 33rd Nat ional Radio Conference (NRSC 2016), pp.
178-187.
[2]. F. Jin, V. Vardharajan and U. Tupakula (Nov,2015),
“Improved detection of primary user emu lation attack in
cognitive radio network”, International Teleco mmun ication
Network and Application Conference (ITNA C), pp. 274-279.
[3]. K. K. Chaunhan and A. K. Singh Sanger(Feb,2014), “Survey
of security threads and Attacks in cognitive radio networks”,
International Conference on Electronics and commun ication
system (ICECS), pp. 01-05.
[4]. K.Lu, H.Ke, J.Yang and L.Zhang(Oct 2012), “Research of
PUE attack based on Location”, 11th International Conference
on signal processing, pp. 1345-1348.
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