Error rate detection due to primary user emulation attack in cognitive radio networks

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 5, October 2020, pp. 5385~5391
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i5.pp5385-5391  5385
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Error rate detection due to primary user emulation attack in
cognitive radio networks
N. Armi1
, W. Gharibi2
, W. Z. Khan3
1
Indonesian Institute of Sciences, Research Center for Electronics and Telecommunication, Indonesia
1
Department of Electrical Engineering, Telkom University, Indonesia
2
Department of Computer Science and Electrical Engineering, University of Missouri-Kansas City, United State
3
Department of Computer and Network Engineering, Jazan University, Kingdom of Saudi Arabia
Article Info ABSTRACT
Article history:
Received Aug 31, 2019
Revised Apr 16, 2020
Accepted Apr 29, 2020
Security threat is a crucial issue in cognitive radio network (CRN).
These threats come from physical layer, data link layer, network layer,
transport layer, and application layer. Hence, security system to all layers in
CRN has a responsibility to protect the communication between among
Secondary User (SU) or to maintain valid detection to the presence of
Primary User (PU) signals. Primary User Emulation Attack (PUEA) is
a threat on physical layer where malicious user emulates PU signal.
This paper studies the effect of exclusive region of PUEA in CRN. We take
two setting of exclusive distances, 30m and 50m, where this radius of area is
free of malicious users. Probability of false alarm (Pf) and miss detection
(Pm) are used to evaluate the performances. The result shows that increasing
distance of exclusive region may decrease Pf and Pm.
Keywords:
Cognitive radio
False detection
Malicious user
Miss detection
Spectrum sensing Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Nasrullah Armi,
Research Center for Electronics and Telecommunication,
Indonesian Institute of Sciences,
Cisitu street, No. 21/154 D, Bandung 40135, Indonesia.
Email: nasrullah.armi@gmail.com
1. INTRODUCTION
Besides sensing and access over imperfect channel [1-3], security threat is crucial issue in cognitive
radio. It comes from most layer such as physical, data link, network, transport, or application layer [4-10].
Unlike traditional wireless networks where operates in fixed spectrum, cognitive radio networks operate in
dynamic spectrum where user opportunistically access available spectrum channel with prior sensing [11-14].
Hence, various factors must be taken into consideration since cognitive radio (CR) deals with the utilization
of unused spectrum in opportunistic manner with the unscheduled appearance of primary users [15].
Physical layer is the lowest level which provides an interface to the medium. Security on physical
layer is related to some possible attack such as intentional jamming attack, primary receiver jamming attack,
primary user emulation attack, and overlapping secondary user attack. Intentional jamming attack means that
malicious user transmit signal intentionally in the existing license bands and jams primary and other
secondary user. The worst case is occurred when malicious users attack in one geographical area and move to
another area very quickly without being identified. The unknown location of primary user is a benefit for
attackers to launch primary receiver jamming attack. The attacker may shift to the primary receiver and
requests transmission from secondary users. This case causes interference to primary receiver.
In CRN, multiple secondary networks perform sensing for available spectrum to access at the same
region with the same time. The transmission from malicious users in one network may cause interference to
the primary and secondary users from the other networks. This threat is known as overlapping secondary user
attack where a malicious user may also imitate the primary user. The fake primary signal is considered as

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 5, October 2020 : 5385 - 5391
5386
original signal by secondary user. Hence, secondary user refrain from transmission or terminate the ongoing
communication and release the frequency spectrum [16]. This fake signal transmitted by malicious user is
known as primary user emulation attack.
Primary user emulation attack is a threat on physical layer. It harms SU to achieve the correct
sensing outcome. Malicious user attacks signal detection by transmitting signal whose has the same
characteristic as primary signal. It causes SU has a difficulty to identify vacant spectrum. Malicious user does
not utilize those vacant bands for its own communication purposes. They only transmit fake primary signal in
vacant bands to obstruct SU from detecting original one.
The existing works related to this issue have been done. Smart attacker was introduced in [17].
The authors use Markov chain technique to model the activities of attackers. Then, energy detection of
attackers was investigated in [18]. The authors used NI-USRP 2922 devices to derive probability of detection
and false alarm. Chen et al in [19] investigated the attackers which is considered as interference to result
inaccurate position of primary user location. The authors implemented directional antenna to explore position
of primary transmitter. The used parameter like angle, arrival time, and strength of signal is explored to
detect an accurate position. An analytical of successful probability to attact primary user location was firstly
studied by authors in [20]. Fading was considered into account to calculate successful probability of
attackers and define a lower limit by applying Fenton’s and Markov approximation, respectively.
CR signaling was introduced by authors in [21] to ease accessing available spectrum efficiently.
The investigation proved that the framework is able to maximize SU transmission rate with low probility of
miss detection and probability of false alarm. Using the statistical characteristic of users to prevent PUEA
issue was proposed by Ghaznavi et al [22].
The used method on this article was well proved that its technique is able to increase the detection
performance compared with the existing works. Investigation of primary user attackers with blind
information of users was presented by authors in [23]. They investigated miss detection rate both
theoretically and experimentally different number of SU. However, the disavadvantages of this study such as
ignoring probability of false detection and the received power performance performance were not clearly
presented. The technique to reduce the impact of attackers to primary user due to selfish and malicious users
was introduced by authors in [24]. They implemented game strategy to counter the attacker and reduce
the error rate detection of primary user. The effect of number of attackers to the performance of detection in
PUEA was studied in [25, 26] with Neyman Pearson decision tecnique. They concluded that increasing
number of attacker may decrease the performance.
This paper explores more performance affected by radius of exclusive region. Two different radius
of exclusive region values is considered with two hypothesis of Neyman Pearson technique to decide
the existence of primary user. Error rates detection such as false alarm and miss detection are used to present
the derived performance evaluation. The rest of the paper is organized as follows. Section 2 discusses system
and numerical model considered in the simulation. The derived result and discussion are presented in
section 3. Finally, conclusion is taken briefly in Section 4.
2. PRIMARY USER EMULATION ATTACK MODEL
The major issue in primary signal detection is how SUs can identify fake primary signal.
Primary user emulation attack (PUEA) is one threat on physical layer in cognitive radio network where
attacker transmit fake primary signal. Two types of well-known PUEA is selfish PUEA and malicious
PUEA. Attackers maximize its bandwidth by preventing other SUs to use the spectrum bands in selfish
PUEA. Attackers transmit signal to resemble the primary signal. Whereas the aim of attackers in malicious
PUEA is obstructing SU to identify the vacant spectrum bands, so that they cannot use those bands. Two type
of errors caused by PUEA are probability of false alarm (Pf) and miss detection (Pm). These two detection
errors are used as main parameter for performance evaluation.
Figure 1 describes a brief concept of how attackers interfere primary user detection. As figure,
there are a number of attackers located on a circular network with radius R and single secondary user located
at the center. Each attackers transmit signal among each other. The attackers (M) are distributed randomly
covering secondary user with a certain radius R. These attackers transmit signal independently. On the other
side, secondary user is located at the center of network with the distance Dp from primary transmitter.
Fixed location of primary transmitter possibly to transmit signal with power Pt. Meanwhile, secondary user is
free attackers at radius Ro where this region is known as exlusive area of secondary user.

Int J Elec & Comp Eng ISSN: 2088-8708 
Error rate detection due to primary user emulation attack in cognitive radio networks (N. Armi)
5387
R
Ro
Dp
Secondary user
Malicious user
Primary transmitter
Figure 1. Primary user emulation attack (PUEA) model
As described in figure, we have a number of attackers located at (𝑟𝑗, 𝜃𝑗) where j is value with range
range 1≤j≤M. Then probability density function (PDF) of 𝑟𝑗 is derived as follows [10].
𝑝(𝑟𝑗) =
2𝑟 𝑗
𝑅2−𝑅𝑜2 𝑅𝑜 ≤ 𝑟 ≤ 𝑅 (1)
where 𝜃𝑗 is equally distributed at range (−𝜋, 𝜋). Then, the received power of transmitted signal from primary
transmitter is derived as bellows:
𝑃𝑟(𝑝)
= 𝑃𝑡 𝑑 𝑝
−2
𝐺 𝑝
2
(2)
where 𝐺𝑝2
= 10
𝜀𝑝
10, 𝜀𝑝~𝑁(0, 𝜎𝑝2). Since 𝑃𝑡 and 𝑑 𝑝 have a fixed values, then the PDF of 𝑝𝑟(𝑝)
uses a log
normal distribution and can be calculated as follows:
𝑃𝑟(𝑝)(𝛾) =
1
𝛾𝐴𝜎 𝑝√2𝜋
𝑒𝑥𝑝 {
(10𝑙𝑜𝑔10 𝛾−𝜇 𝑝)2
2𝜎 𝑝
2 } (3)
where 𝐴 =
𝑙𝑛10
10
and 𝜇 𝑝 = 10𝑙𝑜𝑔10 𝑃𝑡 − 20𝑙𝑜𝑔10 𝑑 𝑝
The total power received from attackers at the secondary user is derived by:
𝑃𝑟(𝑚)
= ∑ 𝑃𝑚 𝐷𝑗
−4
𝐺𝑗
2𝑀
𝑗=1 (4)
where 𝐷𝑗 and 𝐺𝑗
2
are the distance and shadowing between 𝑗 𝑡ℎ
malicious user and secondary user,
respectively.
𝐺𝑗
2
= 10
𝜀 𝑗
10 where 𝜀𝑗~𝑁(0, 𝜎2
𝑚). The right side of equation (4) above is log normally distributed
random variable of the form 10
𝜔 𝑗
10 where 𝜔𝑗~𝑁(𝜇 𝑗, 𝜎2
𝑚), where 𝜇 𝑗 is derived by the following equation:
𝜇 𝑗 = 10𝑙𝑜𝑔10 𝑃𝑚 − 40𝑙𝑜𝑔10 𝐷𝑗 (5)
The PDF of 𝑝𝑟
𝑚
considered as positions of all attackers can be derived as bellows:
𝑃𝑥|𝑟
(𝑚)
=
1
𝑥𝐴𝜎 𝑀√2𝜋
𝑒𝑥𝑝 {
(10𝑙𝑜𝑔10 𝑥−𝜇 𝑀)2
2𝜎 𝑀
2 } (6)
𝑟 is defined as the vector elements of 𝑟1, 𝑟2,…, 𝑟 𝑚. Then, 𝜎2
𝑀 and 𝜇 𝑀 are derived as the equation bellows:
𝜎 𝑀
2
=
1
𝐴2 𝑙𝑛 [1 +
(𝑒 𝐴2 𝜎 𝑚
2
−1) ∑ 𝑒
2𝐴𝜇 𝑗𝑀
𝑗=1
(∑ 𝑒
𝐴𝜇 𝑗𝑀
𝑗=1 )
2 ] (7)

 ISSN: 2088-8708
5388
𝜇 𝑀 =
1
𝐴
𝑙𝑛(∑ 𝑒 𝐴𝜇 𝑗𝑀
𝑗=1 ) −
𝐴
2
(𝜎 𝑀
2
− 𝜎 𝑚
2) (8)
The PDF of the received power from attackers is calculated as follows:
𝑃 𝑚
(𝑥) = ∫ ∏ 𝑃𝑥|𝑟
(𝑚)
(𝑥|𝑟)𝑝(𝑟𝑗)𝑑𝑟𝑗
𝑀
𝑗=1
𝑅
𝑅𝑜
(9)
Futhermore, calculation of the receiver power from attacker uses log normally distributed random
variable with 𝜇 𝑥 and 𝜎𝑥 as bellows:
𝑝 𝑚
(𝑥) =
1
𝑥𝐴𝜎 𝑥√2𝜋
𝑒𝑥𝑝 {−
(10𝑙𝑜𝑔10 𝑥−𝜇 𝑥)2
2𝜎 𝑥
2 } (10)
The values of 𝜇 𝑥 and 𝜎𝑥 can be derived when 𝑝𝑟
𝑚
is considered as a log normally distributed
random variable as written in equations bellows:
𝜎𝑥
2
=
1
𝐴2 (𝑙𝑛𝐸 [(𝑝𝑟
(𝑚)
)
2
] − 2𝑙𝑛𝐸[𝑝𝑟
(𝑚)
]) (11)
𝜇 𝑥 =
1
𝐴
(2𝑙𝑛𝐸[𝑝𝑟
(𝑚)
]) −
1
2
𝑙𝑛𝐸 [(𝑝𝑟
(𝑚)
)
2
] (12)
From (6), the values of 𝑝𝑟
𝑚
and 𝐸[𝑝𝑟
𝑚
|𝑟] is derived by the following equation:
𝐸[𝑝𝑟
(𝑚)
|𝑟] = 𝑀𝑒 𝐴𝜇 𝑗 ∗ 𝑒
𝐴2 𝜎 𝑚
2
2 (13)
where
𝜇 𝑗 = 10𝑙𝑜𝑔10(𝑃𝑚 ∗ 𝐷𝑗
−4
) (14)
𝑒 𝐴𝜇 𝑗=𝑒 𝐴10𝑙𝑜𝑔10(𝑃 𝑚∗𝐷 𝑗
−4)
= 𝑃𝑚 ∗ 𝐷𝑗
−4
(15)
Hence,
𝐸[𝑝𝑟
(𝑚)
|𝑟] = 𝑀𝑃𝑚 ∗ 𝐷𝑗
−4
∗ 𝑒
𝐴2 𝜎 𝑚
2
2 (16)
By integrating equation over range of 𝑟1, 𝑟2,…, 𝑟 𝑀, it becomes:
𝐸[𝑃𝑟
(𝑚)
] = ∫ 𝑀𝑝(𝑟𝑗)𝑃𝑚 ∗ 𝐷𝑗
−4
∗ 𝑒
𝐴2 𝜎 𝑚
2
2 𝑑𝑟𝑗
𝑅
𝑅𝑜
= ∫ 𝑀𝑃𝑚 𝑒
𝐴2 𝜎 𝑚
2
2 ∫
2𝑟 𝑗
𝑅2−𝑅𝑜2
𝑅
𝑅𝑜
𝑅
𝑅𝑜
∗ 𝐷𝑗
−4
𝑑𝑟𝑗 (17)
If secondary user is located at coordinate (0, 0), meaning that 𝐷𝑗 = 𝑟𝑗
𝐸[𝑃𝑟
(𝑚)
] = 𝑀𝑃𝑚 𝑒
𝐴2 𝜎 𝑚
2
2 ∫
2𝑟 𝑗
𝑅2−𝑅𝑜2 ∗
1
𝑟 𝑗
4 𝑑𝑟𝑗
𝑅
𝑅𝑜
𝐸[𝑃𝑟
(𝑚)
] =
𝑀𝑃 𝑚
𝑅2 𝑅𝑜2 𝑒
𝐴2 𝜎 𝑚
2
2 (18)
Using decision criterion of Newman Pearson, we assume that M1 as primary signal transmission in
progress, and M2 is emulation attack in progress. This study implements two error rate for performance
evaluation, those are false alarm and miss detection. False detection means mistake detection by secondary
user where malicious user as attacker signal is detected as primary signal. Meanwhile, miss detection means
secondary user detect primary user signal as attacker signal. Hence, secondary user is unaware and loss an
opportunity to access available spectrum. The decision variable is derived by including the received signal
power as follows:

5389
Λ =
𝑝 𝑚(𝑥)
𝑝(𝑝 𝑟)(𝑥)
(19)
The decision is achived by comparing Λ with the threshold as bellows:
Λ ≤ 𝜆 𝐷1 : Primary signal transmission
Λ ≥ 𝜆 𝐷2 : Emulation attack in progress
Then, error rate is determined by the following decision rule:
𝑃{𝐷2|𝑀1} = probability of missed detection, where decides 𝐷2 when 𝑀1 is true.
𝑃{𝐷1|𝑀2} = probability of false alarm, where decides 𝐷1 when 𝑀2 is true.
Those mentioned error rate for final decision above can be written mathematically as follows:
𝑃{𝐷2|𝑀1} = ∫ 𝑝(𝑝 𝑟)
(𝑥)𝑑𝑥 = 𝛼Λ≥𝜆
(20)
𝑃{𝐷1|𝑀2} = ∫ 𝑝(𝑚)
(𝑥)𝑑𝑥Λ≤𝜆
(21)
3. RESULTS AND ANALYSIS
This section discusses the derived result and performance analysis for the exclusive region effect of
PUEA. We considered two distances for radius of exclusive region, 30 m and 50 m from the center of
network cell. As described in Figure 1, a brief concept how malicious user as attacker interfere primary
signal detection, with a certain number of malicious users located out of exclusive region transmitting fake
primary signal. It is assumed that fix secondary user stand at the center of circular network cell with radius
R = 500 m, 𝑃𝑚 = 4 𝑊, 𝜎 𝑝 = 8 𝑑𝐵, 𝜎 𝑚 = 5.5 𝑑𝐵, primary transmitter power 𝑃𝑡 = 150 𝑘𝑊 and the distance
from primary transmitter to secondary user Dp = 100 km. Number of attackers is set to M = 10 users.
Firstly, we define radius of exclusive region into Ro = 30m. Simulation is executed by 500 times.
Probability of false alarm is derived as shown in Figure 2. Its performance is varied from 0.0046 to 0.0087 as
a function of simulation times. The average value is achieved around 0.0057. Meanwhile, the performance of
probability of miss detection is shown in Figure 3. Simulation is executed by 500 run times. The derived
results are varied from 0.0055 to 0.0075 as function of simulation times. However, the average value is
achieved around 0.0063.
Figure 2. Probability of false alarm during 500
simulation times with parameter R = 500 m,
Ro = 30 m and number of attackers M = 10
Figure 3. Probability of miss detection during 500
Secondly, we replace the distance of radius of exclusive region by 50 m. This study observes
the effect of exclusive region to the performance of detection due to attackers by changing the radius of
exclusive region. The performance results are derived as shown in figures. Figure 4 shows the performance
of probability of false detection with radius of exclusive region Ro = 50 m. The values are varied from
0.0025 to 0.0065 as a function of simulation times. The average value is achieved at 0.0033. Probability of
false detection decreases when compared with the case of Ro = 30 m. Performance of probability of miss

 ISSN: 2088-8708
5390
detection is shown in Figure 5. Its result is fluctuated from 0.0029 to 0.0047. However, the average result is
achieved around 0.0033. Furthermore, CDF of false alarm and miss detection probability is presented in
Figure 6 and Figure 7. The derived results show that the larger radius of exclusive region is used, then
achieve less false alarm and miss detection probabilities.
Figure 4. Probability of false alarm during 500
Figure 5. Probability of miss detection during 500
Figure 6. CDF of false alarm probability (Pf) Figure 7. CDF of miss detection probability (Pm)
4. CONCLUSION
The effect of radius of exclusive region in primary user emulation attack under Neyman Pearson
techniques has been studied. The statistical model for false alarm and miss detection probability is also
discussed. Exclusive region is an area where secondary user free of attackers. The simulation shows that
increasing radius of this area can decrease either probability of false detection or miss detection. The larger
radius of exclusive region is taken influences to achieve less false alarm and miss detection rate. This study
considered one primary transmitter which is located at a certain distance (100 km) away from secondary user.
For further study, it will be considered two or multiple primary base station with different distances.
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