Download Defending Primary User Emulation Attack in Cognitive Radio

International Journal on Emerging Trends in Technology (IJETT)
ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456)
Volume 3 | Issue 2 | July - 2016 | Special Issue
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Defending Primary User Emulation
Attack in Cognitive Radio Networks
Rajesh D. Kadu1, Dr. Pravin P. Karde2
[email protected], [email protected]
Research Scholar, Computer science and Engineering, Information Technology2,
SGB Amravati University, Amravati, India1, Government Residential Woman's Polytechnic, Yavatmal, India 3,
ABSTRACT
In cognitive radio network, an un-licensed user can use a
vacant channel in a spectrum band of licensed user.
Spectrum sensing is one of the critical function in
spectrum management. Cognitive radio is the promising
technology to solve the problem of spectrum scarcity in
which the secondary users can sense the spectrum and
utilize the licensed bands when the spectrum is not being
utilized by the primary user. Many malicious secondary
users can generate the signals similar to primary
transmitter to confuse the good secondary users into
thinking that a primary transmission is in progress. The
good secondary users then will vacate the spectrum
unnecessarily. The malicious users would then use the
evacuated white space for themselves. This attack by
malicious secondary users is called as primary user
emulation attack (PUEA) and it considerably increase the
spectrum access failure probability. In this paper we
analyze the probability density function (PDF) at both
good and malicious users. We also considered the cooperation between the secondary users to detect primary
user emulation attack.
General Terms
Cognitive Radio Networks, Security
Keywords
Cognitive radio Networks, PUE attack, Probability density
function (PDF), malicious user, spectrum sensing .
1. INTRODUCTION
Recently it is observed that, the licensed radio spectrum band
remains under-utilized [1], [2]. Cognitive radio networks [3]
allow the utilization of vacant spectrum in one network by the
users belonging to another network. Primary users are
licensed users to use the allocated spectrum. Secondary users
having no license to use the spectrum can use the spectrum
while primary user is not using it. This can improve the
spectrum utilization significantly and solve spectrum scarcity
problem. These secondary users sense the spectrum to detect
empty spectral bands to utilize for communication. While
utilizing these empty band, secondary users need to avoid the
interference with primary user. If secondary user detects the
presence of primary user after sensing the spectral band, it
must switch to other band. If any other secondary user is using
the same sensed band then spectrum should be shared fairly
by using coexistence mechanism. A malicious secondary user
may modify the air interface of a cognitive radio (CR) to
mimic a primary user signal’s characteristics, thereby causing
good secondary user to falsely believe on presence of primary
user. Good secondary user then vacate the occupied spectrum
band for malicious secondary user believing that it is a
primary user. This attack by malicious secondary users in
cognitive radio networks is known as Primary User Emulation
Attack (PUEA). Primary User Emulation Attack (PUEA) can
be categorized as either a selfish PUE attack or a malicious
PUE attack depending on intension behind launching it [7]
[8].
Selfish PUEA: The aim of an attacker behind this attack is to
maximize its own spectrum usage. When selfish PUE
attackers sense the empty spectrum band, they start
transmitting signals that imitate the signal characteristics of
primary user signals. As a result, other secondary users stops
from competing for the same spectrum band.
Malicious PUEA: The aim of an attacker behind this attack
is to block the dynamic spectrum access process of good or
legitimate secondary users. This attack thwart legitimate
secondary users from detecting and using empty licensed
spectrum bands leading to denial of service.
Matched filter detection, cyclostationary feature detection,
and energy detection are the methods that can be used by good
secondary users to sense the presence of the primary user in
the spectrum. [4], [5]. Among these methods, the energy
based detection is the most common way of spectrum sensing
due to its low computational and implementation
complexities. Energy based detection can be carried out in
both the time and frequency domains. For the matchedfiltering processing, cognitive radio need to have perfect
knowledge of the primary users signaling features. These
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International Journal on Emerging Trends in Technology (IJETT)
ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456)
Volume 3 | Issue 2 | July - 2016 | Special Issue
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features includes bandwidth, center frequency, modulation
type and order, pulse shaping and frame format. The
cyclostationary feature detection method can differentiate
modulated signals, interference, and noise in low SNR ratios.
It can reduce the processing requirements and maintain a
decent detection error probability [6].
In [8], two mechanisms have been proposed to detect PUEA.
The distance ratio test (DRT) consider the correlation between
transmitter receiver distance and the received signal strength.
The distance difference test (DDT) is based on signal phase
difference. Both DRT and DDT consider the transmitter
verification procedure which uses a location verification
method to make a distinction between primary signals and
secondary signals. Both method may be unsuccessful if the
attacker is near to TV tower and transmitting. In [7], author
proposed a robust non-interactive localization method as a
defense against PUEA. This localization based defense
(LocDef) scheme uses received signal strength(RSS) which
consider relationship between
signal strength and a
transmitter location. Transmitter location is calculated by
smoothing the collected RSS measurements and obtaining the
RSS peaks.
Authentication of the primary user's signals using
cryptographic and wireless link signatures via a helper node
is suggested in [12]. In this approach, a helper node is
positioned physically close to a primary user. Secondary user
verifies cryptographic signatures carried by the helper node’s
signals and then obtain the helper node’s authentic link
signatures to verify the primary user’s signals.
The network model proposed in [13] assume the presence of
single attacker and single defender. Both the attacker and
defender can apply estimation techniques and learning
methods to obtain the key information of the environment and
thus design better strategies. Also demonstration of advanced
attack strategy using invariant of communication channels
which defeats the naive defense technique focusing only on
the received signal power is provided.
In [9], proposed localization method use the Time Difference
of Arrival (TDOA) and Frequency Difference of Arrival
(FDOA). TDOA method give motion vector as input to FDOA
method, which in turn identify the precise location of the
transmitting source. In [10], fingerprinting has been used to
authenticate the transmission source. The Fingerprinting
approach in [17] operates by erasing the modulation of all
received signals to get the carrier with phase noise.
Fingerprint of the signal is generated by applying wavelet and
higher-order statistics analysis. This fingerprint is used for
transmitter identification to secure against PUE attacks.
In [11], authors studied the approaches for mitigating the
attacks that can influence the spectral environment in the
PHY layer of a cognitive radio network. A Bayesian game
framework is proposed to analyze and study PUEA [14]. In
this framework users are considered to be uncertain regarding
the legitimacy of the claimed type of other users.
The possibility of a PUEA in cognitive radio networks in a
fading wireless environment was studied analytically in [15].
This was the first analytical approach which was based on
Fenton’s approximation and Markov’s inequality used to
conclude a lower bound on the probability of successful
PUEA. A first analytical study of PUEA is given in [16] in the
presence of multiple malicious users in fading wireless
environments. Neyman-Pearson composite hypothesis test and
Wald’s sequential probability ratio test analyzed to detect
PUEA in fading wireless channels in the presence of multiple
randomly located malicious users.
The key idea behind cooperative sensing is to improve the
sensing performance by exploiting the spatial diversity in the
observations of spatially located CR users. In cooperative
spectrum sensing, CR users exchange
their sensing
information with other users for making a combined decision
more precise than the individual decisions [21].
2. SYSTEM MODEL
Consider the system model [16] in figure 1. The distribution
of secondary users is within a circular grid of radius R. The
primary transmitter is present at a minimum distance d p from
all the users. We consider energy based detection mechanisms
to detect the presence of the primary user. If the received
signal strength is -93 dB m then energy based detection
methods conclude the presence of the primary user [19].
There are M malicious users in the system and they transmits
at power ‘��’. Primary user transmits at power ‘��’ and ��
≫
≫
�� . The secondary user is present at the center
of the exclusive region. The positions of the secondary and the
malicious users are randomly distributed in the circular grid of
radius R and their positions are statistically independent of
each other. The transmission from primary transmitter and
malicious users undergo path loss, log normal shadowing and
Rayleigh fading. The Rayleigh fading is assumed to be
averaged out and can hence be ignored [16].
Fig 1: A typical cognitive radio network in a circular grid
of radius R consisting of good secondary users and
malicious secondary users
For any secondary user fixed at polar co-ordinates (r 0,θ0), no
malicious user are present within a circle of radius centered at
(r0,θ0). The path loss exponent chosen for transmission from
primary transmitter is 2 and from malicious user are 4. No
malicious users are present within a circle of radius ��, called
as the exclusive radius from secondary user. There is no cooperation between the secondary users.
¿ 10εp
10
¿ e Aεp
2
Gp
2
Gp
is the shadowing random variable
from primary transmitter and
A=
ln10
10
, �p
represents the logarithmic shadowing in the unit of dB with a
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International Journal on Emerging Trends in Technology (IJETT)
ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456)
Volume 3 | Issue 2 | July - 2016 | Special Issue
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2
σp
zero mean and variance
2
0, σ p
εp N ¿
distribution
¿ e Aεj
).
Similarly
A=
ln10
10
(−π , π ) ∀
j. The
received power at the secondary user from the primary
transmitter is given by
(p)
−2
2
Pr =Pt d p G p (2)
, �j represents the logarithmic
shadowing in the unit of dB with a zero mean and variance
σ 2j
Otherwise
Where θm is uniformly distributed in
G2j =10εj10
is the shadowing random variable from malicious
user and
0
following a normal
following a normal distribution
2
j
0, σ
εj N ¿
). As
random variable is log normally distributed and takes only
positive real values, the probability density function (PDF) of
received powers follows a log- normally distribution.
3. ANALYTICAL MODEL
Due to absence of cooperation between the secondary users,
probability of PUEA on every user in the networks is same.
Hence we analyze the probability function (PDF) of the
received signal of one secondary user.
Where
2
p
G =10
εp
10
0, σ 2p
εp N ¿
,
). Since Pt and dp
are fixed, the PDF of the received power at the secondary user
from the primary transmitter follows a log-normal distribution
and can be written as:
γ −μ p
10 log 10 ¿
¿
¿2
¿(3)
−¿
¿
1
(p)
exp ¿
Pr ( γ ) =
A σ p √ 2 πγ
where
γ
is random variable and
A=
μ p=10 log 10 pt −20 log 10 pd p
ln10
10
and
(4)
The total received power at the secondary user from all the
malicious users is given by:
M
Pr =∑ Pm d j G j (5)
(m )
Fig 2 : Scenario with transformed co-ordinates. The
secondary user of interest is at (0,0). Malicious users are
uniformly distributed in the annular region (R0,R). The
primary is at (dp, θp).
Malicious users coordinates are transformed such that the
secondary user of concern lies at the origin (i.e., at (0, 0)). The
transformed co-ordinates of the primary will then be (dp, θp).
As dp >> R and thus it is acceptable to approximate the coordinates of the primary user to be (dp, θp) regardless of which
secondary user we consider for the analysis. Figure 2 shows
this scenario [16].
3.1 Probability Density Function of
Received Signal
probability density function (PDF) of r j, P(rj),
given by [16], [18] :
2r j
2
2
P(rj) = R −R0
rj ∈ [R0, R]
∀j
is
2
j=1
where dj is the is the distance between the jth malicious user
and the secondary user.
G2j
is the shadowing between the
jth malicious user and the secondary user.
0, σ 2j
εj N ¿
εj
G2j =10 10
,
).
Each term in the summation in the right hand of equation (5)
is a log-normal distributed random variable of the
μ j , σ 2m ).
wj N ¿
μ j =10 log 10 pm −40 log 10 pd j (6)
form
Let M number of malicious users are positioned at coordinates (rj, θj) 1 ≤ j ≤ M. The position of the jth malicious
user is uniformly distributed in the annular region between R 0
and R, and rj and θj are statistically independent . The
−4
10wj
10
The PDF of
and
)
P(m
r
conditioned on the positions of all M
malicious user can be written as:
(1)
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x−μ M
10 log 10 ¿
¿
¿2
¿ (7)
−¿
¿
1
m
exp ¿
Pxǀr =
A xσ p √ 2 π
where r is the vector with elements r1 to rm. The expression for
PDF can be written as:
x−μ x
10 log 10 ¿
¿
¿2
¿(8)
−¿
¿
1
m
exp ¿
P (x )=
A xσ x √2 π
If
)
P(m
r
μx
and
is log normally distributed random variable then
σ 2x
can be written as:
P(rm)
¿
P(rm)
E [¿]
¿−2 ln ¿ (9)
E¿
ln ¿
1
2
σ x= 2 ¿
A
used, determining signal modulation and estimation of the
position of transmitters and receivers. After sensing the
environment, the sensing results are used to determine radio
settings. Secondary users may experience multipath fading
and shadowing as wireless channels are prone to it. Due to
this multipath fading and shadowing, secondary users may fail
to accurately sense the presence of primary user signals.
Consequently, without interference to primary users, spectrum
access cannot be possible for secondary users. Collaborative
spectrum sensing solve this problem. In collaborative
spectrum sensing, sensing results of several secondary users
are combined to improve the probability of detecting the
primary user. Co-operation among cognitive radio networks is
now regarded as a key technology for dealing with the
problems in a practical implementation of cognitive radio. The
correctness of primary user detection and improvement in
performance is possible due to such co-operation among
cognitive nodes. This collaboration is achieved by exchanging
the information among cognitive radios, carrying out tasks
cooperatively, negotiating with peers and considering peer
information to conclude about their operating settings.
Currently it is assumed that, secondary users are honest in
exchanging such information during collaborative spectrum
sensing. But some malicious secondary users can report false
sensing information about spectrum sensing in order to use
vacant spectrum. Although collaborative spectrum sensing in
dynamic spectrum access (DSA) seems to be promising
approach to improve the utilization of underutilized licensed
spectrum bands, it requires the secondary users should not
infringe any acceptable interference bounds specified by the
primary users.
In section 2 and 3, we considered the non-cooperation
between secondary users. The detection of PUEA is based on
individual secondary user's sensing result. There is no sharing
of sensing observations among secondary users. Spectrum
sensing based on energy detection method consider the
predefined threshold (γ). If the received power at good
secondary user is below this threshold then spectrum band is
considered to be vacant. Otherwise secondary user concludes
that primary user is present. We assume that this spectrum
sensing report, good secondary user shares with other
secondary users in the network. Based on the reports from
other secondary users, good secondary users conclude that
spectrum band is vacant or primary user is present. We
assume that, good secondary user can detect whether received
signals are from malicious users or from primary user. A
P(rm)
(¿)
P(rm)
E[¿] ¿2 (10)
1
E[¿]− ln ¿
2
2 ln ¿
1
μx= ¿
A
threshold ( α ¿ is set to measure the suspicious level of
the secondary node. If suspicious level of all secondary nodes
is greater than set threshold α then good secondary user
concludes that primary user is present. Otherwise good
secondary user conclude that primary user emulation attack is
launched.
4. COOPERATIVE SPECTRUM
SENSING
Algorithm:
Sensing of the frequency spectrum comprises the tasks like:
determination of when and which frequency bands are being
1.
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Let the predefined threshold is γ.
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If good secondary user find the received signal
above set threshold γ then concludes signal is either
from primary user or malicious user. If primary user
is present then sensing result is broadcasted to all N
secondary users in network.
3.
If good secondary user observe the presence of
malicious user then received signal is due to PUEA,
STOP. Else go to step 4.
4.
6
x 10
8
7
6
5
4
3
2
1
0 0
If calculated suspicious level of each secondary user
> threshold
α
then
a)
Good secondary user concludes that
primary user is present, else
b)
The good secondary user concludes that
malicious user is present and PUEA is
launched.
5. SIMULATIONS
Figure 3 show the PDF of the received power at the secondary
user when the primary transmitter is at distance (d p) 150Km.
In this simulation, considered parameter values are, R = 1Km,
R0 = 50m, M = 30, P t = 150Kw, Pm = 5w and
m=¿
σ¿
p=¿
σ¿
8dB,
simulation
computation
9
Good secondary user receive the results from all N
secondary users and calculate the suspicious level of
each secondary user.
5.
10
PDF of received power
2.
1
2
3
4
5
6
­6
Received power at the secondary receiver from malicious users: x 10
Fig 4: PDF of received power at the secondary receiver
from malicious users
6. CONCLUTION
In this paper, we considered analytical approach to find
probability density function (PDF) of received power at the
secondary users. This received power at secondary user is
considered from malicious users and from the primary
transmitter as well. we assumed no co-operation between
secondary users. The obtained experimental result of PDF
considerably matches with theoretical result. We have also
presented the approach to detect PUEA by assuming the cooperation between the secondary users.
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