Int. J. Communications, Network and System Sciences, 2011, 4, 507-513
doi:10.4236/ijcns.2011.48062 Published Online August 2011 (http://www.SciRP.org/journal/ijcns)
A Two Step Secure Spectrum Sensing Algorithm Using
Fuzzy Logic for Cognitive Radio Networks
Ehsan MoeenTaghavi, Bahman Abolhassani
School of Electrical Engineering, Iran University of Scien c e and Technology (IUST), Tehran, Iran
Received June 5, 2011; revised July 9, 2011; accepted July 18, 2011
Abstract
In this paper, a two step secure spectrum sensing algorithm is proposed for cognitive radio networks. In this
algorithm, the sensing results of secondary users are pre-filtered and applying fuzzy logic, so, the overall
sensing performance of the network is improved. To determine pre-filter parameters, statistical parameters of
the sensing results are used to remove those sensing results which are far from the majority sensing results.
However, to obtain a better performance in the spectrum sensing, we propose a fuzzy logic to nullify the ef-
fects of malicious users who transmit false sensing data to the fusion center. We further propose a Fuzzy
Trust Level for each user as to weight the sensing result of the corresponding user before combining all
sensing results in the fusion center. Simulation results demonstrate that our proposed algorithm yield signifi-
cant improvement in the performance of the spectrum sensing and identifying malicious users.
Keywords: Cognitive Radio, Cooperative Spectrum Sensing, Energy Detection, Malicious User Detection,
Pre-filtering, Fuzzy Logic
1. Introduction
Traditionally, fixed spectrum bands have been assigned
to specific services for a long time. This policy has led to
inefficient spectrum usage, and caused cognitive radio
networks were proposed. In these networks, cognitive
radios, which are called secondary (unlicensed) users are
allowed to use the primary (licensed) users’ bands when
such bands are unoccupied by primary users (Pus).
However, the secondary users (SUs) must make the band
vacant immediately after a PU starts transmitting in the
corresponding band [1]. Therefore, the most important
task of a cognitive radio is spectrum sensing. Spectrum
sensing techniques include energy detection, cyclosta-
tioary feature detection and matched filter detection [2-4].
The performance of a spectrum sensing is determined by
two probabilities: probability of detection (Pd) and prob-
ability of false alarm (Pf). Pd is the pro bability of declar-
ing the chann el is occupied while the PU is present. Pf is
the probability of declaring the channel is occupied by a
PU while the PU has no transmission. Among different
spectrum sensing schemes for reliably identifying the
licensed spectrum status, the energy detector scheme
incurs a very low implementation cost and therefore is
widely used. It serves as the optimal method to detect the
signal transmitted by a PU whose location is unknown,
and the detector only knows the power of the received
signal [6]. The problem of this scheme is that the re-
ceived signal power can be seriously weakened at a par-
ticular geographical location du e to multipath fading and
shadowing effects [7]. In these circumstances, it is diffi-
cult for a single sensing user to distinguish between an
idle band and a deep faded one. In order to overcome this
problem, cooperative spectrum sensing schemes have
been proposed [5,8,9]. However, in cooperative sensing,
due to imperfect channel between a primary user and a
secondary user (SU) or dishonestly behavior of a SU, a
user might send false sensing result to the fusion center.
So, the performance of the system degrades severely. To
overcome this problem, secure spectrum sensing has
been proposed.
In [8], a spectrum sensing data falsification problem
was solved by weighted sequential probability ratio test
(WSPRT), which gives a good performance. However,
this method requires the knowledge of locations of sens-
ing terminals and position of PU for obtaining some re-
quired prior probabilities. This is inappropriate for mo-
bile cognitive radios and for systems in which the loca-
tion of the primary user is completely unknown. In [10],
a robust secure distributed spectrum sensing scheme is

E. MOEENTAGHAVI ET AL. 508
proposed that uses robust statistics to approximate the
distribution for both hypotheses of all users, discriminat-
ingly, based on their past data report. Th e authors in [11]
propose the majority rule in the fusion center to nullify
the effects of the malicious users. In [5], an effective
weighted combining method is proposed to reduce the
impact of false information. In [12], a defense scheme is
proposed that computes suspicious levels and trust values
of the users. In our previous work [13], malicious user
detection based on outlier energy detection techniques is
proposed and a filtering method is used based on statis-
tical parameters of sensing results to eliminate the effects
of malicious users. In this paper, we propose a two step
secure spectrum sensing algorithm. At first, based on the
statistical parameters of SUs sensing results, a pre-filter
is designed to remove the sensing results of the secon-
dary users which are far from the others. Then, trust
weighted values are assigned to the users whose sensing
results are passed from the filter based on the fuzzy logic.
Finally, a weighted combining method is proposed to
make final decision in the fusion center.
The rest of the paper is organized as follows: In Sec-
tion 2, the system model is described and a brief back-
ground about fuzzy logic is presented. In Section 3, our
new algorithm to nullify the effects of malicious users is
proposed. Simulation results are carried and analyzed in
Section 4, and conclusions are drawn in Section 5.
2. System Description
2.1. System Model
We discuss a cognitive radio network consist of one PU
and a group of N SUs. A cooperative spectrum sensing is
considered in which channels between a primary user
and each secondary user have independent and identi-
cally distributed (i.i.d) Rayleigh distributions. Variation
in path-loss is neglected. Each SU conducts energy de-
tection and transmits the measured value of received
signal energy in a perfect (i.e. error free) control channel
to the fusion center. By combining the sensing results
received from different SUs, the fusion center makes the
final decision regarding the presence or absence of the
primary user. If en[k] for n=1,2, ···, N represents the re-
ceived signal energy of nth SU at time instant k, hy-
potheses H1 and H0 denote the presence and absence of
the primary signal, respectively. Then, the signal energy
received by nth SU is given by:

 

12
1
12
0
d,
d,
k
k
k
k
TT
nn
T
nTT
n
T
htstztt H
ek
zt tH











(1)
where T represents the time interv al of sensing, s(t) is the
primary signal, hn(t) denotes the channel gain between
the primary user and the nth secondary user, and zn(t) is
the additive white Gaussian noise (AWGN).
A threat to cooperative spectrum sensing is transmis-
sion of false results by attackers to the fusion center,
which leads to make a wrong decision (SSDF1 attack).
So, cooperative spectrum sensing in adversarial envi-
ronments where a malicious user sends false data de-
grades the performance of the system severely. In adver-
sarial environments, different kinds of malicious users
can affect the spectrum sensing system. They may send
data indicating the presence of the PU to the fusion cen-
ter (“Always Yes” malicious users). These malicious
users cause the fusion center to erroneously decide that
the PU is present. Then, malicious users selfishly use the
entire free spectrum band and also probability of false
alarm is increased. Another kind of malicious are those
who always send data indicating the absence of the pri-
mary user (“Always No” malicious users). This kind of
malicious users causes the interference among the pri-
mary and secondary user’s signal and decrease probabil-
ity of detection [14]. Therefore, the fusion scheme must
be robust enough. In this paper, we propose a two step
algorithm for secure spectrum sensing. In step 1, the
sensing results in the data collector are pre-filtered and in
step 2, we assign each user a Fuzzy Trust Level using
fuzzy logic. In our proposed algorithm, more reliable
users are assigned with higher trust levels. This algo-
rithm also detects suspicious users with their corre-
sponding suspiciou s levels using fuzzy logic. Finally, the
sensing results are combined together based on their
Fuzzy Trust Levels in the fusion center.
2.2. Overview of Fuzzy Logic
In this section, we present a brief background on fuzzy
logic. The reason for that is because our proposed algo-
rithm integrates fuzzy logic with spectrum sensing in
order to better detect malicious user.
Fuzzy logic was introduced by Dr. Lotfi Zadeh of
UC/Berkeley in the 1960’s as a mean to model the un-
certainty of natural language. fuzzy logic, a widely de-
ployed technology for developing sophisticated control
systems [15,16], provides a simple way to get definite
precise conclusion and solution based on unclear, impre-
cise, ambiguous or missing input information. Figure 1
shows the steps that fuzzy logic controller is composed
of. The steps of a fuzzy logic can be summarized as fol-
lows: 1) receiving input values representing measure-
ments of the parameters to be analyzed; 2) subj ecting the
input value to if-then fuzzy rules; 3) averaging and
1Spectrum Sensing D ata Falsification Attack.

E. MOEENTAGHAVI ET AL. 509
Figure 1. Components of the fuzzy logic controller [17].






 
med1.5 ,
med1.5 .
u
l
ekk k
ekk k


 



 (2)
weighting the results from all individual rules into one
single output decision; 4) defuzzification of output to get
a value between 0 and 1. To develop a fuzzy logic con-
troller, two major components are required: 1) definition
of a membership function for each input/output parame-
ter; 2) designing the fuzzy rules. The membership func-
tion is a graphical representation of the magnitude of
participation of each input. The fuzzy logic rules use the
input membership values as weighting factors to deter-
mine their influence on the output sets [17].
Considering the number of malicious users is much
less than the total number of users, the median is less
vulnerable to the presence of the malicious users. So, the
filtering removes most of the malicious users.
Although filtering removes most malicious users,
some sensing results, which have been affected by fading
or by malicious users, might pass through filtering. The
performance of the system will degrade in these two
cases. To prevent this performance degradation, we pro-
pose to dedicate a trust factor (TF) to each user who
passed through filtering. The users, which have been
affected by fading or are malicious, are assigned with
lower trust factors. The users, which have a good channel
condition or aren’t suspicious to be malicious are as-
signed with higher trust factors. Trust factors are deter-
mined and normalized so that their summation becomes
one, i.e.
For several reasons, fuzzy logic is very appropriate for
using on secure spectrum sensing. One reason is that
there is no clear boundary between normal and anomaly
users. The use of fuzziness of fuzzy logic helps to
smooth the abrupt separation of normality and abnormal-
ity. Another reason is the reduction of miss detection and
false alarm probabilities. In the next section, we present
details of the fuzzy logic that we use in our secure spec-
trum sensing algorithm.
3. Secure Spectrum Sensing Algorithm

11.
N
n
n
TF k



(3)
By filtering and removing sensing results of malicious
users, performance of the system can be compensated to
some extent. First, the sensing results are passed through
a pre-filter. In our proposed algorithm, those sensing
results that are numerically distant from the rest of the
results are not considered in the final decision. We pro-
pose to use the median (med[k]) and standard deviation
(


k 
) of energy values of sensing results in the com-
putation of upper and lower bounds of the filtering, at
time instant k, as follows:
In this paper we propose to use fuzzy logic to dedicate
each user a Trust Factor. As mentioned before, the fuzzy
logic is composed of membership functions for each the
input/output variables and fuzzy values. We select spec-
trum sensing results as input parameters to the fuzzy
controller in order to detect maliciou s users. For an input
parameter, three Gaussian membership functions are
designed: 1) always no malicious users; 2) trusted users;
and 3) always yes malicious users. Figure 2 shows the

E. MOEENTAGHAVI ET AL. 510
Figure 2. Membership functions of an input parameter.
three Gaussian membership functions for an input pa-
rameter. The output parameter also has two Gaussian
membership functions distributed in the range [0, 1] as
shown in Figure 3. These two membership functions are
called Fuzzy Trust Level (FTL) and Fuzzy Suspicious
Level (FSL). After defining the input parameters, the
fuzzy logic rules are designed. These rules are written
depending on the knowledge of secure spectrum sensing.
We discuss these rules in the following:
1) If (the sensing result is Trusted user) then (output is
FTL). This rule presen ts the sensing result of this user is
an expected value, so this user is normal and participates
in final decision in the fusion center;
2) If (the sensing result is always no malicious user) or
(the sensing result is always yes malicious user) then
(output is FSL), this rule presents the sensing result of
this user is an unexpected value, so th is user is malicious
and takes no part in final decision.
The output of the system shows the trust and suspi-
cious levels of each user. Those users whose sensing
results are near the median of the sensing results are as-
signed with higher trust levels and other users whose
sensing results are far from the median are assigned with
higher suspicious levels in the fuzzy logic. After De-
fuzzification for each user, we have a value for FTL and
a value for FSL in the range [0, 1]. The FTL shows the
degree of being a normal user and the FSL shows how
much malicious the user might be. In final decision, to
eliminate the effects of malicious users, we propose to
combine each sensing result, considering its fuzzy trust
level (FTL). First, we normalize fuzzy trust level of user
n at time instant k as follows:




1
FTL
FTL ,
FTL
n
nN
n
n
k
k
k





(4)
where N denotes the number of users which have been
Figure 3. Membership functions of the output.
passed through filtering. Then, the final decision is
computed using FTLs of all N users as follows:
 
1
0
1FTLee.
H N
nn T
nH
kk




(5)
If the value obtained in th e left side of the above equ a-
tion is greater than a given threshold (eT), the fusion cen-
ter will announce the presence of the primary signal. In
our proposed algorithm, the suspicious users who are
malicious or their sensing results are affected by fading
are assigned with lower fuzzy trust levels, so their effects
on the final decision are insignificant.
To achieve a better performance, the sensing results of
each user over a given number of (say L) measurements
are considered to obtain the final fuzzy trust level for
each user. In the computation of a fuzzy trust level, we
assign higher weights to those FTLs which are closer to
the present time, k, i.e.:




1
0
FTLFTL .
L
n
l
kLl k



n
l



(6)
Finally, these weighted fuzzy trust levels are normal-
ized according to Equation (4).
By considering previous and present behaviors of each
user in computation of the final fuzzy trust level, the us-
ers which behave maliciously for a period of time and
behave normally the rest of time, are detected and as-
signed with lower Fuzzy Trust Levels.
4. Simulation Results
In this section, the proposed secure spectrum sensing
algorithm is evaluated by simulations. The basic pa-
rameters are fixed and considered as a group of N = 50
secondary users. The mean received SNR of the channel
between the primary user and each of secondary users is
2 dB. Independent and identically distributed small scale

E. MOEENTAGHAVI ET AL. 511
fading channels are considered between any SU and the
PU, and the path loss is neglected.
In Figure 4, we assume a cooperative system with 10
“Always No” malicious users, each giving a value indi-
cating the absence of the primary user. To evaluate our
secure sensing algorithm, we compare probability of
detection (Pd) and probability of false alarm (Pf) of our
algorithm and three other cases, which are: 1) coopera-
tive spectrum sensing with no malicious user; 2) spec-
trum sensing with malicious users with no suppression;
and 3) secure spectrum sensing proposed in [5]. From
Figure 4 we can see that using our secure sensing algo-
rithm, the “Always No” malicious users are assigned
with low fuzzy trust levels and can not affect the per-
formance of the system. So, the probability of detection
(Pd) of the system would be close to that of cooperative
sensing with no malicious user.
In Figure 5, unlike Figure 4, we consider a sensing
system in which 10 users always announce the presence
of the primary user to the fusion center. From Figure 5
we can see that our proposed scheme can nullify the ef-
fects of malicious users in the final decision and has bet-
ter performance compared to those of previous works.
It’s notable that in the secure sensing, which was pro-
Figure 4. Probability of detection and false alarm in adversarial environment with 10 “Always No” malicious users.
Figure 5. Probability of detection and false alarm in adversarial environment with 10 “Always Yes” malicious users.

E. MOEENTAGHAVI ET AL. 512
posed in [5], the fading channel is not considered. By
applying the fading effects to this scheme, due to its fil-
tering structure, some malicious users affect the final
decision.
In Figure 6, we observe the probability of detection
according to the number of “Always No” malicious users.
From this figure, we can see that our proposed algorithm
is more robust than traditional ones. Our algorithm is
robust until 50% of the secondary users become mali-
cious and has a better performance compared to that of
[5].
In Figure 7, we observe the probability of false alarm
with varying the number of “Always Yes” malicious
users. As shown in the figure, our algorithm with effec-
tive malicious user detection can achieve an acceptable
performance.
5. Conclusions
In this paper, a new cooperative secure spectru m sensing
05 10 15 20 25 30 35
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Number of mali ci ous users
P robability of det ec t i on
P roposed Al go rithm
No malicious detection
Existing method [5]
Figure 6. Probability of Detection with varying the number of “Always No” malicious users.
510 15 2025 30 35 40
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Number of mal ici ous users
P robability of F als e A larm
Proposed A l gorit hm
No malicious detect ion
Exis ti ng m ethod [5]
Figure 7. Probability of False Alarm with varying the number of “Always Yes” malicious users.

E. MOEENTAGHAVI ET AL. 513
algorithm for malicious user detection in cognitive radio
networks based on fuzzy logic was proposed. In our
proposed secure sensing algorithm, first a pre-filtering is
designed to prevent the users, whose sensing results are
far from the others, take effect on final decision. Then,
based on the results of the filter output, the fuzzy pa-
rameters are obtained, and then according to fuzzy pa-
rameters, a fuzzy trust level is assigned to each user. Fi-
nally, the sensing results are combined in the fusion cen-
ter based on their fuzzy trust levels. Simulation results
show that our proposed algorithm can significantly nul-
lify the effects of malicious users. Moreover, it can alle-
viate the effect of fading channels. Furthermore, the
complexity of our proposed algorithm is much lower
than those of existing ones. In future work, we will de-
velop our secure sp ectrum sensing algorithm for the case
of using cyclostationary detectors (rather than energy
detectors used in this paper) and for more complex sce-
narios.
6. Acknowledgements
The authors would like to thank the Iranian Institute of
information and Co mmunication Technology (th e former
ITRC) for the financial support of this work.
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