computers and electrical engineering...neural network” for the consideration to publish in the...
TRANSCRIPT
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Computers and Electrical Engineering
PRIMARY USER EMULATION ATTACK MITIGATION USING NEURAL NETWORK--Manuscript Draft--
Manuscript Number: COMPELECENG-D-20-00080R3
Article Type: Research Paper
Keywords: Cognitive Radio Primary User Emulation Attack neural network software defined radio energy detector spectrum sensing
Corresponding Author: VIJAYAKUMAR PONNUSAMY
INDIA
First Author: VIJAYAKUMAR PONNUSAMY
Order of Authors: VIJAYAKUMAR PONNUSAMY
Kottilingam K
Karthick T
Mukeshkrishnan M.B
Malathi D
Tariq Ahamed Ahanger
Abstract: The spectrum sensing scheme suffers from a physical layer attack of Primary UserEmulation Attack (PUEA). The resolution is to mitigate the cognitive radio user from thePUEA under the physical layer. Detecting the PUEA attack in real-time is a challengingone. The traditional Location-based PUEA detection requires the primary user'slocation knowledge, which may not be possible practically. This research focuses ondeveloping a reliable spectrum sensing mechanism in the presence of PUEA attackand rapid change in the wireless channel. This reliable spectrum sensing framework isdeveloped using the neural network-based PUEA detector excluding the locationinformation. The Software-Defined Radio (SDR) called Universal Software RadioPeripheral (USRP) 2943R is used to implement the proposed mechanism for analyzingperformance in real-time. The real-time experimental results show that PUEA detectioncan be achieved with 97% accuracy.
Response to Reviewers: Response to editor comments
1.EDIT THE PAPER CAREFULLY. You must use a native English-speaking editor.Papers with less than excellent English will not be published even if technically perfect.Examples:- Abstract: "Primary User Emulation Attack (PUEA) is physical layer attack in, which disables the cognitive radiouse the unused spectrum. ??"Response :A native English-speaking editor edited the paper, and the correction version isattached under review reports.2. The paper's title is too long and cumbersome. It should be concise and as short aspossible. Response :The title is renamed with less number of words3. Do not use any acronyms in the Conclusion!Response :The acronyms in the Conclusion section are removed
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From
Vijayakumar Ponnusamy
Associate professor/ECE Department
SRM Institute of Science & Technology, Chennai, India
To
Journal Editors and reviewers
International Journal of Computers & Electrical Engineering
Dear Editors/ reviewers
Thank you very much for the reviewers and editor for their valuable comments for improving
the article quality. I thank the editor and reviewer for the conditional acceptance of the article.
I have addressed all the editor comments and enhanced the article. Here, I am submitting the
revised article entitled “PRIMARY USER EMULATION ATTACK MITIGATION USING
NEURAL NETWORK” for the consideration to publish in the International Journal of
Computers & Electrical Engineering. I confirm that this work is original and has not been
published elsewhere, nor is it currently under consideration for publication elsewhere. I have
no conflicts of interest to disclose. Please address all correspondence concerning this
manuscript to me at [email protected].
I am looking forward to your encouraging response.
Sincerely
Vijayakumar Ponnusamy
Cover Letter
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Response to editor comments
1.EDIT THE PAPER CAREFULLY. You must use a native English-speaking editor. Papers with less than excellent English will not be published even if
technically perfect. Examples:
- Abstract: "Primary User Emulation Attack (PUEA) is physical layer attack
in , which disables the cognitive
radio use the unused spectrum. ??"
Response :
A native English-speaking editor edited the paper, and the correction version is
given on the next page.
2. The paper's title is too long and cumbersome. It should be concise and as short as
possible.
Response :
The title is renamed with less number of words
3. Do not use any acronyms in the Conclusion!
Response :
The acronyms in the Conclusion section are removed
Response to Reviewers
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PRIMARY USER EMULATION ATTACK MITIGATION
USING NEURAL NETWORK
Vijayakumar Ponnusamy, Associate professor,ECE Department,SRM Institute of Science
and Technology, Kattankulathur,Chennai.E-Mail: [email protected]
KottilingamKottursamy, Associate professor, IT Department, SRM Institute of Science and Technology, Kattankulathur, Chennai. E-Mail: [email protected]
Karthick. T, Assistant professor, IT Department, SRM Institute of Science and Technology,
Kattankulathur, Chennai. E-Mail: [email protected]
M.B. Mukeshkrishnan, Associate professor, IT Department, SRM Institute of Science and Technology, Kattankulathur, Chennai. E-Mail: [email protected]
D.Malathi, Professor, Kongu Engineering College, Perundurai,Erode.
E-Mail: [email protected]
Tariq Ahamed Ahanger, Associate Professor,College of Computer Engineering and Sciences,
PrinceSattam Bin Abdulziz University, KSA. E-Mail: [email protected]
Abstract
Primary User Emulation Attack (PUEA) is the physical layer attack in the
spectrum sensing, which disables the legislative cognitive radio use the unused
spectrum. The spectrum scheme is peculiar in the selection mode and the
decision-making happens by the perception of reducing the possibility of
colliding with the primary user. The resolution is to mitigate the primary user
from the Primary User Emulation Attack (PUEA) under the physical layer
concerning the cognitive radio. The spectrum sensing which disables the
legislative cognitive radio control of the available spectrum. Detecting the PUEA
attack in real-time is a challenging one. The traditional Location-based PUEA
detection requires the primary user's location knowledge, which may not be
possible practically. This research focuses on developing a reliable spectrum
sensing mechanism in the presence of PUEA attack and rapid change in the
wireless channel. This reliable spectrum sensing frameworkis framework is
developed using the neural network-based PUEA detector without excluding the
location informationandan information and an energy detector. The Software-
Defined Radio (SDR) called Universal Software Radio Peripheral (USRP) 2943R
is used to implement the proposed mechanism for analyzing performance in real-
time. The real-time experimental results show that PUEA detection can be
achieved with 97% accuracy.
Keywords: Cognitive Radio, energy detector, Primary User Emulation Attack, neural
network, spectrum sensing, software-defined radio
mailto:[email protected]:%[email protected]:[email protected]:[email protected]
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1. Introduction
The security of cognitive radio is an important one significant to realize the dynamic
spectrum access, especially in the physical and medium layer level. Even though there
are many security issues in cognitive radio, the Primary User Emulation Attack
(PUEA) hasa tremendousimpacton dynamic spectrum access applications.There are
many methods proposed in the literature to overcome PUEA. The detection of PUEA
is carried out by recognizing the action in the frequency domain [1]. This approach
employed a Fast Fourier Transform (FFT) across the operation of wireless networks.
It useda neural network The neural network is utilized to classify the PUE and primary
user users based on a rational database. A random frequency hopping anti-jamming
scheme called dogfight isproposed [2] in which the Cognitive Radio(CR) has to select
a random channel to sense to avoid PUEA and solve the problem by the formulation
of a zero-sum game.The drawback of this approach is it requires the channel statics
called availability probability, which may not be possible in practice. Certain
drawbacks of this approach are based on availability and probability which are not
possible to process in real-time. A non-parametric Bayesian classifier is proposed to
detect the Primary User Emulation (PUE) signal based on the fingerprint of the device
on the Orthogonal Frequency Division Multiplexing (OFDM ) signal [3]. The carrier
frequency difference, the phase shift difference, the second-order cyclostationary,
and the amplitude of the received signal is used as the fingerprint feature of the device
to identify the PUE. The fingerprint feature of the device is employed to identify the
PUE based on the carrier frequency difference, the phase shift difference, and the
second-order cyclo stationary. The amplitude of the received signal which paves the
way to identify the fingerprint feature of the device. In a multipath Rayleigh fading
channel scenario detecting PUE signal based on the channel-tap power is
proposed[4], .but it is only proposed[4] and applicable on simulation in a real-time
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system. tracking the multipath channel coefficient and computing the channel tap filer
order is really challenging one. The challenges are faced by tracking the multipath
channel coefficient and computing the channel tap filer order. The selfish PUE attack
detection is carried out by employing a channel surveillance mechanism [5]. The
problem with this approach is it requires extra sensing node to apply the channel
surveillance. To solve the problem of this approach it is required to have an additional
sensing node to surveillance the channel. A Selfish selfish PUE is considered with
some surveillance strategy[6]. Under this model, the network manager has the
responsibility of monitoring the attacker and follows a surveillance strategy and
analyzes the performance through the Strong Stackelberg Equilibrium (SSE).
Numerical results suggest the network manager significantly enhances its utility for
playing a Nash equilibrium Equilibrium (NE) strategy. An analytic work on the PUE
attack is presented [7] and derived the optimal spectrum access function by
maximizing the secondary transmission data rate under miss missing detection and
false alarm constraints. PUEA's impact on cooperative spectrum sensing is studied
[8]. An optimally weighted scheme handles the problem of the PUEA attacker. The
optimal weights are extracted by maximizing the Secondary User (SU) throughput by
protecting the Primary User (PU) from interference and facing the PUEAs.
A fast and reliable PUE detection algorithm is proposed using an energy detector and
location as a two-level database. The admission control approach is employed to
mitigate the attack. [9]. An energy-efficient double threshold mechanism is proposed,
where the presence of the PUEA is taken as a constrain of the optimization problem
and solved by maximizing the energy efficiency [10].
A defense mechanism against the PUE attacker using an adaptive Bayesian learning
automaton algorithm is proposed [11]. The proposal uses two different channels
simultaneously to make quickly with learning in non-stationary environments and
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selects the optimal channel for the given time slot. Under the scheme, the Secondary
User (SU) uses an uncoordinated frequency hopping (UFH) and sends its data on
different channels selected in the learning process. The defense mechanism under this
scheme is a random selection of channels that believe the attacker doesn't know the
choice.
A hybrid Genetic Artificial Bee Colony (GABC) algorithm is used [12]to increase the
spectrum utilization by detecting the PUE attacks. The proposed mechanism uses the
Genetic operators with ABC algorithm to trade off e between exploitation and
exploration to research optimal solutions. The mechanism uses two threshold values,
which are compared with the received signal energy of SUs, to differentiate between
PU and PUE.
Another channel hopping-based defense mechanism for PUEA defense is presented
[13]. The defense mechanism is developed using game theory. Under the work,
interactions between the cognitive users and the attacker are formulated as a multi-
player zero-sum game. The solution is obtained by using the Nash equilibrium of the
game. But this model only which is capable of handling a single attacker.
SDR or SDN implementation based spectrum sensing and sharing facilitate
programmability[14-20], which can be used for the implementation of the neural
network for PUEA detection.
In this work, a neural network-based PUEA detection with energy detection spectrum
sensing is presented. The contributions of this article are the neural network-based
PUEA detection is carried out with real-time Software Defined Radio (SDR)
hardware USRP. A reliable spectrum sensing method is proposed with a lookup table
combined with an energy detector and neural network-based PUEA detection.
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1) Neural network-based PUEA detection is carried out with real-time Software-
defined Radio (SDR) hardware USRP
2) Proposes a reliable spectrum sensing method with a lookup table combined with
energy detector and neural network-based PUEA detection
The benefit of the proposed approach in the Cognitive radio can be summarized as
below
1. The proposed reliable spectrum sensing scheme enables detection of PUE attack,
thereby the overall spectral utilization and spectral usage by CR will be improved.
2. The CR radio will have more opportunity to transmit, and throughput of individual
CR will be increased.
3. There is a possibility of PU getting interference because of the attacker. By
detecting the attacker by the proposed approach, we can eliminate the attacker and the
interference to the PU.
The benefit of the proposed approach in the cognitive radio can be summarized as the
proposed reliable spectrum sensing scheme enables detection of PUE attack, thereby
the overall spectral utilization and spectral usage by CR will be improved. The CR
radio will have more opportunity to transmit, and the throughput of individual CR will
be increased. There is a possibility of PU getting interference because of the attacker.
By detecting the attacker by the proposed approach the attacker and the interference
to the PU are eliminated.
The remaining part of the article is organized as follows: Section 2 gives the system
model and the methodology.; Section 3 presents the result and discussion. Section 4
concludes the article research work with a summary of the work with the future
direction of research.
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2. SystemModel and Methodology
The system for the experimental study is given in figure 1. The system model for the
investigation is depicted in Figure 1. The system model consists of three-node, namely
Primary User (PU), Primary User Emulation Attacker (PUEA), and Cognitive Radio
(CR), which has a neural network to detect the attacker. Figure 2 shows the
experimental setup used for the implementation of the neural network-based classifier
to identify the PUEA signal.The PXIe chassis with two vector signal generator and
vector signal analyzer is configured as a 2x2 MIMO PU transmitter and receiver. One
The first USRP RIO 2943R is configured as a Secondary User (SU) receiver, which
receives signals of both PU signal and PUEA and employs the neural network
classification.The second USRP is configured to act like a perform similar to a PUEA
attacker.
Figure 1. System Model
Primary user
Attacker
Cognitive
radio
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Figure 2. Experimental Setup
Figure 3. Signal Processing of Cognitive Radio
The signal processing block diagram of the cognitive radio is given in figure Figure
3. The RF front end of the USRP hardware receives the signal, down-convert it,
converts the analog signal into digital data, and stores in the receiver buffer. The RF
front end of the USRP hardware receives the signal and performs the conversion
operation internally. And also converts the analog signal into digital data which are
stored in the receiver buffer. The data from the receiver buffer is used to training train
the neural network in the training phase and used also managed to classify the data
into three classes, namely primary signal, noise, and attacker signal.
Figure 4. Neural Network Architecture
Figure 4 shows the neural network architecture utilized for the classification problem,
which consists of 4 hidden layers with 15 neurons at each layer, having one input
layer, and one output layer. The number neuron in the input layer is equal to the length
of the input feature vector . since here, Considering 300 samples of energy are fed at
a time, and the number input layer neurons are about 300. This simple architecture is
used for less computational complexity. The sigmoidal activation function is used at
RF Front
end Receiver
buffer
BPN
Neural
network
1-Busy
0- Free Reliable
spectrum
sensing
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applied to all layers for computing except the output layer, where the softmax layer is
used utilized for the three classes.
In this feed-forward network, the ith neuron net valueℎ𝑖𝑘 at kth hidden layer is given
as
ℎ𝑖𝑘 = 𝑏𝑖
𝑘 + ∑ 𝑤𝑗𝑖𝑘𝑡𝑘−1
𝑗=1 𝑜𝑗𝑘−1 𝑓𝑜𝑟 𝑖 = 1,2. . 𝑡𝑘 (1)
Where 𝑏𝑖𝑘 is the bias component of the ithneuron at kth hidden layer;𝑤𝑗𝑖
𝑘 is the weight
vector between the ith and jth neuron at the kth layer ;𝑜𝑗𝑘−1 is the output of input at k-
1th hidden layer from the jth neuron.
The output of the hiddenlayer hidden layer 𝑜𝑖𝑘is
𝑜𝑖𝑘 = 𝑎(ℎ𝑖
𝑘)𝑓𝑜𝑟 𝑖 = 1. . 𝑡𝑘 (2)
Where a() referee the activation function operation ; ℎ𝑖𝑘 is the net value of ith neuron
at the kth hidden layer
The net value ℎ1𝑚at the output layer and the final output y is
ℎ1𝑚 = 𝑏1
𝑚 + ∑ 𝑤𝑗1𝑘𝑡𝑘−1
𝑗=1 𝑜𝑗𝑘−1 𝑓𝑜𝑟 𝑖 = 1,2. . 𝑡𝑘 (3)
𝑦 = 𝑎(ℎ1𝑚) (4)
Where a( ) is the activation function , 𝑤𝑗1𝑘 is the weight vector between jth and lth
neuron at the kth layer; here sigmoidal activation function is used for all layer used
except the output layer, where the softmax layer is used accepted for the three classes.
The hypothesis𝜓 to detect the PUEA is given as
𝜓 = {
𝑖𝑓 𝑦 = 1; 𝑃𝑈𝐸𝐴 𝑖𝑓 𝑦 = 0; 𝑃𝑈
𝑖𝑓 𝑦 = 2; 𝑛𝑜𝑖𝑠𝑒 } (5)
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The outcome of the PUEA detection is combined with the energy detector spectrum
sensing to make obtain the correct decision on spectrum sensing. Since the energy
detector only provides less reliability in the low SNR case, it can be emulated very
quickly, which degrades the performance more comparing other mechanisms.; the
The energy detector is used as a sensing algorithm. Other methods can't are not
feasible to be emulated easily, and the chance of an attack is less, and even the attack
is present, the degradation of performance is negligible. The spectrum sensing
outcome 𝐷 with the energy detector is
𝐷 = {0(𝑃𝑈 𝑝𝑟𝑒𝑠𝑒𝑛𝑡); 𝑖𝑓 𝐸𝑡𝑒𝑠𝑡 > 𝑡ℎ 𝑎𝑛𝑑 𝑦 = 0 𝑜𝑟 𝑦 = 1
1(𝑃𝑈 𝑎𝑏𝑠𝑒𝑛𝑡); 𝑖𝑓 𝐸𝑡𝑒𝑠𝑡 < 𝑡ℎ 𝑎𝑛𝑑 𝑦 = 2 𝑜𝑟 𝑦 = 1} (6)
Where 𝐸𝑡𝑒𝑠𝑡 is the average energy calculation in the given frequency, and 𝑡ℎ is the
threshold value used in the energy detector algorithm. An experiment is conducted in
the indoor lab with various transmitting power settings on the PU transmitter; then,
the multiple threshold values are calculated using the median value of the maximum
and minimum energy received in the hardware. The average value of these multiple
measurements is taken as a final threshold value, which can detect the PU even
through PU transmit on various transmit power. The entire process is given as an
algorithm which is given below
Algorithm: ReliableSpectrum Sensing with PUEA Detectionusing NeuralNetwork
Training phase
Initialization: Make an initial delay of 𝑡𝑑 to make the three SDR to synchronization of transmission and reception and after that make CR SDR acquire data on power trigger mode
Step1: Make PUE attacker send data from a sequenced frequency the set (1.2 GHz to 6 GHz)
with step size increment of frequency 20Mhz from 1.2 GHz to 6GHz.
Step2: Make PU send data from a sequenced frequency in the set (1.2 GHz to 6 GHz) with step
size increment of frequency 20Mhz from 1.2 GHz.
Step3: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step size
increment of frequency 20Mhz.
Step4: calculate Calculate energy from the I/Q sample for all the above-received frequency
using a window size of M=1024 samples as 𝑓𝑝𝑢𝑝𝑢𝑒 =1
𝑀∑ ∑ 𝑠𝑝𝑢𝑝𝑒𝑎(𝑛)𝑒
−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and
make this a feature 𝑓𝑝𝑢𝑝𝑢𝑒set when both PU and PUE attacker presence.
Step4:switch Switch off PU and Make only PUE attacker send data from a sequenced
frequency the set(1.2 GHz to 6 GHz) with step size increment of frequency 20Mhz from 1.2
GHz to 6GHz
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Step5: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step size
increment of frequency 20Mhz.
Step 6: calculate Calculate energy from the I/Q sample for all the above-received frequency
using a window size of 1024 samples as 𝑓𝑝𝑢𝑒 =1
𝑀∑ ∑ 𝑠𝑝𝑒𝑎(𝑛)𝑒
−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and make this a
feature 𝑓𝑝𝑢𝑒set when PUE attacker only presence. Step7:switch Switch off PUE attacker and Make only PU send data from a sequenced
frequency the set(1.2 GHz to 6 GHz) with step size increment of frequency 20Mhz from 1.2
GHz to 6GHz
Step8: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step size
increment of frequency 20Mhz.
Step 9: calculate Calculate energy from the I/Q sample for all the above-received frequency
using a window size of 1024 samples as 𝑓𝑝𝑢 =1
𝑀∑ ∑ 𝑠𝑝𝑢(𝑛)𝑒
−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and make this a
feature 𝑓𝑝𝑢set when PUE attacker only the presence
Step10:switch Switch off both PU and PUE attacker
Step11: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step
size increment of frequency 20Mhz.
Step 12: calculate Calculate energy from the I/Q sample for all the above-received frequency
using a window size of 1024 samples as 𝑓𝑛 =1
𝑀∑ ∑ 𝑤(𝑛)𝑒−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and make this a
feature 𝑓𝑛set when PUE attacker only presence. Step 13:trainTrain the neural network using the feature set 𝑓𝑝𝑢𝑝𝑢𝑒 , 𝑓𝑝𝑢𝑒 , 𝑓𝑝𝑢 𝑎𝑛𝑑𝑓𝑛
Operational phase
Step1: Make PUE attacker send data by a random frequency from the set (1.2 GHz to 6 GHz)
with step size increment of frequency 20Mhz from 1.2 GHz to 6GHz.
Step2: Make PU send data by a random frequency from the set (1.2 GHz to 6 GHz) with step
size increment of frequency 20Mhz from 1.2 GHz.
Step3: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step size
increment of frequency 20Mhz.
Step4: calculate Calculate energy from the I/Q sample for all the above-received frequency
using a window size of M=1024 samples as 𝐸𝑡𝑒𝑠𝑡 =1
𝑀∑ ∑ 𝑠𝑟𝑎𝑛𝑑𝑜𝑚(𝑛)𝑒
−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and
apply this to the neural network for classification and energy detector for reliable detection.
Step5: using Using the neural network output y and 𝐸𝑡𝑒𝑠𝑡 the final spectrum sensing decision is given calculated as
𝐷 = {0(𝑃𝑈 𝑝𝑟𝑒𝑠𝑒𝑛𝑡); 𝑖𝑓 𝐸𝑡𝑒𝑠𝑡 > 𝑡ℎ 𝑎𝑛𝑑 𝑦 = 0 𝑜𝑟 𝑦 = 1
1(𝑃𝑈 𝑎𝑏𝑠𝑒𝑛𝑡); 𝑖𝑓 𝐸𝑡𝑒𝑠𝑡 < 𝑡ℎ 𝑎𝑛𝑑 𝑦 = 2 𝑜𝑟 𝑦 = 1}
The final spectrum detection is made obtained from the lookup table given below.
Table 1 .Final spectrum detection in the presence of PUEA
Energy detector outcome
D
PUEA detection outcome
𝜓 Final spectrum detection
1(PU present) 0(PUEA absent) 0(no spectrum)
1(PU present) 1(PUEA present) 0(no spectrum )
0(PU absent) 1(PUEA present) 1(use spectrum)
0(PU absent) 0 (PUEA absent) 1(use spectrum)
From table 1, we can observe that based on From Table 1, the investigation is based
on the PUEA detection, and energy detector outcome, and the final spectrum detection
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is made with reliability. Whenever a neural network detects PUEA, then the energy
detector detected it as PU. The lookup table's final decision will ensure that the SU
will get access to the spectrum. So, more spectrum access will be provided to SU from
this scheme.
3. Result and Discussion
The data needed to train the neural network is captured in real-time using the SDR
hardware with proper experimental setup and realizing the PUEA attack. The SDR
hardware can only generate I/Q samples. So, at At the received side, the received I/Q
samples are obtained from the SDR hardware from which the energy values are
calculated as a feature vector for training the neural network. The experimental setup
for realizing the PUEA and collecting the attacker data is summarized below.
The three USRP, one the first one acts as Primary User (PU), one the second one acts
as Cognitive Radio(CR), and the third one acts as PUEA attacker. All the nodes keep
a 10 feet distance from each other. The parameter set used for the experimental study
in real-time using NI USRP 2943R is given in table Table 2. PUEA detection is not
the function of the frequency and only based only on the energy level injected by the
PUEA. But many Many frequency bands are sensed because the attacker may be
trying attempting to attack various bands dynamically. So, the SDR is programmed to
sense many frequency bands. The attacker SDR generates an attack signal randomly
in the range of frequency band from 1.2 GHz to 6 GHz to simulate a realistic scenario.
A dedicated USRP acts as a PUEA attacker and generates attacker data in real-time.
The attacker USRP SDR is programmed to broadcast the string cautiously “I am
PUEA attacker” in the same frequency (2 GHz) of the primary user uses.
Table 2.SDR parameter setting
Parameter PU Attacker value CR value
Transmit
frequency
2GHz to 6GHz 2GHz to 6GHz 2GHz to 6GHz
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Transmit power -10dBm to
+10dBm
10dBm to
+10dBm
10dBm to
+10dBm
I/Q rate 250kbs 250kbs 250kbs
Modulation
scheme
QAM 16 BPSK QAM 32
Sampling rate 10Ms/s 10Ms/s 10Ms/s
Real-time hardware Data data acquired from the primary user USRP RIO and the
PUEA USRP RIO unit for the training. Ten thousand samples of data from both
primary and PUEA hardware is collected as for training, validation, and testing.
purpose. From the collected data set, 70% is used for training, purposes, 15% is used
for validation, and 15% is used for testing. purposes.
To train the neural network, we need PU alone present data, attackeralone present
data, and PU plus attacker presented data. To train the neural network, the PU,
attacker, and PU plus attacker need to manifest the data. Those data are collected by
the CR radio using the following approach.
To collect PU alone training data samples, the attacker USRP kept as an ideal without
transmitting anything, and the PU transmitter alone allowed to send a string “I am
PU” continuously using the parameter setting in table Table 2.This string is decoded
before store the collected data sample to ensure that the data gathered only from PU.
The collected string is decoded before storing the data sample to ensure that the data
gathered solely from PU. Similarly, to obtain the attacker alone, transmitting data is
received by keeping the PU ideal and making attackers continuously send the “I am
PUEA attacker” string in the same frequency of PU. This label is decoded before store
storing the collected data to ensure the data collected is valid data from only PUEA
attackers. The PU and attacker are enabled to transmit the “PU and PUEA “string
continuously to capture both present case data.
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The transmit power of the USRP is varied from -10 dBm to +10dBm in a step size of
5dBm to collect data for the above three cases. This arrangement captures the
variability of the wireless channel.
As another variability, the transmit frequency is changed from 2GHz to 6GHz in a
step size of 0.5 GHz and the three cases of PU alone, PUAK attacker alone, and both
present.
Figure 5.MSE value Vs. Epochs
The Mean Square Error-values for various training, validation, and testing iteration
are is plotted in figure.5 Figure 5 to observe the training performance.It is found from
the figure .5 that for the given training data set after 53 Epochs, the neural network
converged successfully to a classifier PU and PUEA data with minimum MSE
criteria. It is observed from the above figure, that the data set after 53 Epochs and the
neural network are converged successfully to classify the PU and PUEA data with
minimum MSE criteria.
0 10 20 30 40 50
10-2
10-1
100
Best Validation Performance is 0.025011 at epoch 53
Mea
n S
qu
ared
Err
or
(mse
)
59 Epochs
Train
Validation
Test
Best
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Figure 6.Training state
The training sate state of the neural network is observed from the gradient and
validation checks and it is depicted in Figure 6. Figure 6 shows the training state of
the system.The figure proves that training is carried out well in the minimum gradient
direction, and the validation checks. The figure It also confirms that the trained neural
network fails to classify only for a few samples in validation.
Figure 7. Error Histogram
The error histogram values are plotted in figure Figure 7 to illustrate illustrates the
distribution of error values during the training, validation, and testing stage. Figure 7
shows And also records that a large volume of the data samples is getting near to zero
error.It confirmsthat the training of the neural network is done accurately.
10-3
10-2
10-1
100
grad
ient
Gradient = 0.004842, at epoch 59
0 10 20 30 40 500
2
4
6
val f
ail
59 Epochs
Validation Checks = 6, at epoch 59
0
100
200
300
400
500
600
700
800
900
1000
Error Histogram with 20 Bins
Inst
ance
s
Errors = Targets - Outputs
-0.6
051
-0.5
232
-0.4
413
-0.3
595
-0.2
776
-0.1
957
-0.1
139
-0.0
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0.04
987
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0.21
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0.62
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0.70
48
0.78
67
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86
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04
Training
Validation
Test
Zero Error
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The training confusion matrix for the classification problem is computed and given in
figure 8. Figure 8. shows the The classification accuracy and error of the trained neural
network are attained. The diagonal element shows the correct classification; for the
given problem, and it is observed that 96.7% correct classification is carried on the
training phase, 97.3% on validation, 97.8% in the testing phase, and 96.9% in total.
Figure 8. ConfusionMatrix
The confusion matrix for the classification problem is computed and given in figure
8. Figure. 8 shows the classification accuracy and error of the trained neuralnetwork.
The diagonal element shows the correct classification; for the given problem, and it
is observed that 97.1% correct classification is carried on the training phase, 95.7%
on validation, 100% in the testing phase, and 97.3% in total. The off-diagonal
elements show the percentage of miss the missed classification, which is around 0.7%
and 2% in the total case. The above confusion matrix values prove that the neural
network will able to classify the radio as PU and PUEA more accurately.
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Figure 9. Receiver Operating Curve
ReceiverOperatingCurve Receiver Operating Curve (ROC) for the training phase,
validation phase, test phase, and combined all phase results are plotted in
figure.9Figure 9..ROC gives a trade-off between two performance measures of
classifiers called sensitivity and specificity. In the ROC curve, the false positive rate
is on the x-axis, which represents 1 – specificity. The true positive rate is on the y-
axis, which represents 1-sensitivity.
Specificity gives the performance measure of the whole negative part of the data set.
Sensitivity provides performance measures of the entire positive part of the dataset. If
the performance curve is diagonal, then the classifier performance is poor because it
favors both sides of the data equally. If the curve is on the top left side and above the
middle diagonal line, then the performance is good. The ROC curve below the middle
diagonal line shows poor performance.
For the perfect classifier, the ROC curve should align be aligned with the x and y-
axis. Figure-9 Figure 9 ROC curve shows that the performance of the classifier is
perfect in all three stages called training, validation, and test. From all ROC curves in
figure 9, the above mentioned figure it proves that the classifier performs perfectly
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18
because the curves align with the x and y-axis at the top left side above the middle
diagonal line.
Figure 10. Probability of Detection
The PUE attack detection output is combined with an energy detector sensing
algorithm is to calculate the probability of detection and to validate the proposed
method which is described in Figure 10.,which is plotted in figure 10 to validate the
proposed method.The graph gives the average value of the probability of detection,
which is then computed over 50 trials. SNR calculation in real-time SDR is calculated
with the help of Error Vector Magnitude(EVM). The EVM vector can be extracted
from the SDR hardware from which the SNR is calculated using the following
relationship S𝑁𝑅 =1
𝐸𝑉𝑀2. The obtained final result is compared with similar work of
a hybrid Genetic Whale Optimization Algorithm (GWOA)[21]. Figure 10 proves that
the proposed neural network-based PUEA detection and spectrum detection
outperform when comparing with the literature work. While the GWOA takes carries
5dB SNR to achieve probability detection of 1, the proposed scheme can attain it
around 2.5dB. This result archives 2.5dB SNR gain comparing to that of the literature
method to accomplish probability detection of 100%.
4. Conclusion and Future Scope
-20 -15 -10 -5 0 5 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
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SNR
Pro
bab
ilit
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f D
etec
tio
n
proposed work
GWOA
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19
Detection of primary user emulation attack signal is the biggest challenge in the
practical wireless channel, which rapidly changes intern it changes the signal
characteristics of both primary user and primary user emulation attack. Detection of
primary user emulation attack signal is the biggest challenge in the real-time wireless
channel which leads to accomplishing rapid changes in the signal characteristics of
both primary user and primary user emulation attack. This proposed work handled
this issue very well effectively by using neural networks.The problem of detecting the
primary user emulation attack is formulated as a two-class classification problem to
classify the signal into the primary user and primary user emulation attack. The neural
network is designed using four hidden layerswith layers with 15 neurons, one input,
and one output layer. The system setup is carried out with three universal software
radio periphera.l software-defined radio The Software-Defined Radio is to represents
represent the primary user, primary user emulation attack, and cognitive radio with 10
feet from each other. The data collection and training is carried out with different
frequency and transmit power. The receiver operating curves based validation of the
trained model is carried out accomplished. The real-time signal classification is
achieved with an accuracy of 97%, which enablesto use of the proposed approach in
the practical real-time system deployment. The outcome of the neural network is
applied to the energy detector algorithm, and the probability spectrum hole detection
is carried outalso executed. The proposed scheme is also compared with literature,
and it is proved that the proposed method outperforms with 2.5dB gain for a 100%
probability of detection. The detection time is one of the primary factors in cognitive
radio, especially for multiband sensing. The future work will focus on reducing the
decision time by optimizing the proposed system.
Reference
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20
1. Pu, D. and Wyglinski, A.M., 2014. Primary-user emulation detection using database-assisted frequency-domain action recognition. IEEE Transactions on Vehicular
Technology, 63(9), pp.4372-4382.
2. Li, H. and Han, Z., 2010. Dogfight in spectrum: Combating primary user emulation attacks in cognitive radio systems—Part II: Unknown channel statistics. IEEE
Transactions on Wireless Communications, 10(1), pp.274-283.
3. Nguyen, N.T., Zheng, R. and Han, Z., 2011. On identifying primary user emulation attacks in cognitive radio systems using nonparametric bayesian classification. IEEE
Transactions on Signal Processing, 60(3), pp.1432-1445.
4. Le, T.N., Chin, W.L. and Kao, W.C., 2015. Cross-layer design for primary user emulation attacks detection in mobile cognitive radio networks. IEEE
Communications Letters, 19(5), pp.799-802.
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6. Ta, D.T., Nguyen-Thanh, N., Maillé, P. and Nguyen, V.T., 2018. Strategic surveillance against primary user emulation attacks in cognitive radio
networks. IEEE Transactions on Cognitive Communications and Networking, 4(3),
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7. Karimi, M. and Sadough, S.M.S., 2017. Efficient transmission strategy for cognitive radio systems under primary user emulation attack. IEEE Systems Journal, 12(4),
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8. Shrivastava, S., Rajesh, A. and Bora, P.K., 2018. Defense against primary user emulation attacks from the secondary user throughput perspective. AEU-
International Journal of Electronics and Communications, 84, pp.131-143.
9. Yu, R., Zhang, Y., Liu, Y., Gjessing, S. and Guizani, M., 2015. Securing cognitive radio networks against primary user emulation attacks. IEEE Network, 29(4), pp.68-
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10. Das, D. and Das, S., 2017. Intelligent resource allocation scheme for the cognitive radio network in the presence of primary user emulation attack. IET
Communications, 11(15), pp.2370-2379.
11. Mahmoudi, M., Faez, K. and Ghasemi, A., 2020. Defense against primary user emulation attackers based on adaptive Bayesian learning automata in cognitive radio
networks. Ad Hoc Networks, p.102147.
12. Elghamrawy, S.M., 2020. Security in cognitive radio network: defense against primary user emulation attacks using genetic artificial bee colony (GABC)
algorithm. Future generation computer systems, 109, pp.479-487.
13. Ahmadfard, A. and Jamshidi, A., 2019. A channel hopping based defense method against primary user emulation attack in cognitive radio networks. Computer
Communications, 148, pp.1-8.
14. Vijayakumar, P. and Malarvizhi, S., 2017. Wideband full duplex spectrum sensing with self-interference cancellation–an efficient SDR implementation. Mobile
Networks and Applications, 22(4), pp.702-711.
15. Vijayakumar, P. and Malarvihi, S., 2017. Green spectrum sharing: Genetic algorithm based SDR implementation. Wireless Personal Communications, 94(4), pp.2303-
2324.
16. Vijayakumar, P., George, J., Malarvizhi, S. and Sriram, A., 2018. Analysis and Implementation of Reliable Spectrum Sensing in OFDM Based Cognitive Radio.
In Smart Computing and Informatics (pp. 565-572). Springer, Singapore.
17. Ponnusamy, V. and Malarvihi, S., 2017. Hardware Impairment Detection and Prewhitening on MIMO Precoder for Spectrum Sharing. Wireless Personal
Communications, 96(1), pp.1557-1576.
18. Kottursamy, K., Raja, G., Padmanabhan, J. and Srinivasan, V., 2017. An improved database synchronization mechanism for mobile data using software-defined
networking control. Computers & Electrical Engineering, 57, pp.93-103.
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mobile data management system. In 2017 IEEE SmartWorld, Ubiquitous Intelligence
& Computing, Advanced & Trusted Computed, Scalable Computing &
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20. Indukuri, C.L. and Kottursamy, K., Advanced Accident Avoiding, Tracking and SOS Alert System Using GPS Module and Raspberry Pi. In Artificial Intelligence
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21. Elghamrawy, S.M. and Hassanien, A.E., 2019. GWOA: a hybrid genetic whale optimization algorithm for combating attacks in cognitive radio network. Journal of
Ambient Intelligence and Humanized Computing, 10(11), pp.4345-4360.
Vijayakumar Ponnusamy completed Ph.D. from SRM IST, M.E from Anna University and B.E from
Madras University. He is a Certified “IoT specialist” and “Data Scientist.”His current research interests
are Machine Learning, Deep Learning, IoT - intelligent system, and cognitive radio. He is currently
working as Associate Professor in the ECE Department, SRM IST, Chennai, India.
KottilingamKottursamy is an Associate Professor in the School of Computing, SRMIST, India. He
has completed his P.hd from Anna University. He is one of the Co-Principal Investigators of IITK-
SPARC Project, in collaboration with UC Davis, USA. A former Member NGNLab, Anna University,
Chennai, India. His research interest includes Artificial Intelligence, Next Generation Networks, and
Bio-Statistics.
T. Karthick is Assistant Professor in SRMIST, Chennai, India. He holds a Ph.D. Degree from Anna
University and M.Tech from Sathyabama University. He has 11 years of teaching and 5- years of
industry experience. His research areas include IoT, Cloud Computing, Machine learning. He has
published more than 15 Papers in Journal and attended 10 International and national conferences.
M.B. Mukesh Krishnan received his Ph.D. in Computer Science and Engineering, Currently working
as Associate Professor in Department of Information Technology, SRM Institute of Science and
Technology, Chennai, India. His research interest includes Wireless Sensor Networks, Under Water
Sensor Networks, Mobile Computing, Ad hoc networks, Mobile Ad hoc Networks, and Network
Security.
D.Malathi is a Professor in the Department of Electronics and Communication Engineering, Kongu
Engineering College, Perundurai, India. She obtained her B.E degree from Madras University,ME
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22
from Anna University, and a Ph.D. degree from Anna University, Chennai, India. She had published
more than 40 papers in conferences and Journals. Her area of interest includes Low Power VLSI Signal
Processing.
Tariq Ahamed Ahanger is currently an Associate Professor with the College of Computer
Engineering and Sciences, Prince Sattam Bin Abdulaziz University. His interests include the Internet
of Things, cybersecurity, and artificial intelligence.
-
Research highlights
Neural network based PUEA detection in real time on multiple frequencies using
Software defined Radio (SDR) hardware USRP
A reliable spectrum sensing mechanism on multiple frequencies in the presence
of PUEA attack on real time channel using SDR realization
Detecting PUEA attack using a neural network without the location information
Improving overall spectral utilization and spectral usage by CR .
Highlights
-
Graphical Abstract
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1
PRIMARY USER EMULATION ATTACK MITIGATION
USING NEURAL NETWORK
Vijayakumar Ponnusamy, Associate professor, ECE Department, SRM Institute of Science
and Technology, Kattankulathur, Chennai.E-Mail: [email protected]
KottilingamKottursamy, Associate professor, IT Department, SRM Institute of Science and Technology, Kattankulathur, Chennai. E-Mail: [email protected]
Karthick. T, Assistant professor, IT Department, SRM Institute of Science and Technology,
Kattankulathur, Chennai. E-Mail: [email protected]
M.B. Mukeshkrishnan, Associate professor, IT Department, SRM Institute of Science and
Technology, Kattankulathur, Chennai. E-Mail: [email protected]
D.Malathi, Professor, Kongu Engineering College, Perundurai, Erode.
E-Mail: [email protected]
Tariq Ahamed Ahanger, Associate Professor, College of Computer Engineering and Sciences,
PrinceSattam Bin Abdulziz University, KSA. E-Mail: [email protected]
Abstract
The spectrum sensing scheme suffers from a physical layer attack of Primary
User Emulation Attack (PUEA). The resolution is to mitigate the cognitive radio
user from the PUEA under the physical layer. Detecting the PUEA attack in real-
time is a challenging one. The traditional Location-based PUEA detection
requires the primary user's location knowledge, which may not be possible
practically. This research focuses on developing a reliable spectrum sensing
mechanism in the presence of PUEA attack and rapid change in the wireless
channel. This reliable spectrum sensing framework is developed using the neural
network-based PUEA detector excluding the location information. The
Software-Defined Radio (SDR) called Universal Software Radio Peripheral
(USRP) 2943R is used to implement the proposed mechanism for analyzing
performance in real-time. The real-time experimental results show that PUEA
detection can be achieved with 97% accuracy.
Keywords: Cognitive Radio, energy detector, Primary User Emulation Attack, neural
network, spectrum sensing, software-defined radio
1. Introduction
The security of cognitive radio is significant to realize the dynamic spectrum access,
especially in the physical and medium layer level. Even though there are many
security issues in cognitive radio, the Primary User Emulation Attack (PUEA) has a
tremendous impact on dynamic spectrum access applications. There are many
Manuscript File Click here to view linked References
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methods proposed in the literature to overcome PUEA. The detection of PUEA is
carried out by recognizing the action in the frequency domain [1]. This approach
employed a Fast Fourier Transform (FFT) across the operation of wireless networks;
the neural network is utilized to classify the PUE and primary users based on a rational
database. A random frequency hopping anti-jamming scheme called dogfight is
proposed [2] in which the Cognitive Radio (CR) has to select a random channel to
sense to avoid PUEA and solve the problem by the formulation of a zero-sum game.
Certain drawbacks of this approach are based on availability and probability which
are not possible to process in real-time. A non-parametric Bayesian classifier is
proposed to detect the Primary User Emulation (PUE) signal based on the fingerprint
of the device on the Orthogonal Frequency Division Multiplexing (OFDM) signal [3].
The fingerprint feature of the device is employed to identify the PUE based on the
carrier frequency difference, the phase shift difference, and the second-order cyclo
stationary. The amplitude of the received signal which paves the way to identify the
fingerprint feature of the device. In a multipath Rayleigh fading channel scenario
detecting PUE signal based on the channel-tap power is proposed[4] and applicable
on simulation in a real-time system. The challenges are faced by tracking the
multipath channel coefficient and computing the channel tap filer order. The selfish
PUE attack detection is carried out by employing a channel surveillance mechanism
[5]. To solve the problem of this approach, it is required to have an additional sensing
node to surveillance the channel. A selfish PUE is considered with some surveillance
strategy [6]. Under this model, the network manager has the responsibility of
monitoring the attacker and follows a surveillance strategy and analyzes the
performance through the Strong Stackelberg Equilibrium (SSE). Numerical results
suggest the network manager significantly enhances its utility for playing a Nash
Equilibrium (NE) strategy. An analytical work on the PUE attack is presented [7] and
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derived the optimal spectrum access function by maximizing the secondary
transmission data rate under missing detection and false alarm constraints. PUEA's
impact on cooperative spectrum sensing is studied [8]. An optimally weighted scheme
handles the problem of the PUEA attacker. The optimal weights are extracted by
maximizing the Secondary User (SU) throughput by protecting the Primary User (PU)
from interference and facing the PUEAs.
A fast and reliable PUE detection algorithm is proposed using an energy detector and
location as a two-level database. The admission control approach is employed to
mitigate the attack. [9]. An energy-efficient double threshold mechanism is proposed,
where the presence of the PUEA is taken as a constrain of the optimization problem
and solved by maximizing the energy efficiency [10].
A defense mechanism against the PUE attacker using an adaptive Bayesian learning
automaton algorithm is proposed [11]. The proposal uses two different channels
simultaneously to make quickly with learning in non-stationary environments and
selects the optimal channel for the given time slot. Under the scheme, the Secondary
User (SU) uses an uncoordinated frequency hopping (UFH) and sends its data on
different channels selected in the learning process. The defense mechanism under this
scheme is a random selection of channels that believe the attacker doesn't know the
choice.
A hybrid Genetic Artificial Bee Colony (GABC) algorithm is used [12]to increase the
spectrum utilization by detecting the PUE attacks. The proposed mechanism uses the
Genetic operators with ABC algorithm to tradeoff between exploitation and
exploration to research optimal solutions. The mechanism uses two threshold values,
which are compared with the received signal energy of SUs, to differentiate between
PU and PUE.
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Another channel hopping-based defense mechanism for PUEA defense is presented
[13]. The defense mechanism is developed using game theory. Under the work,
interactions between the cognitive users and the attacker are formulated as a multi-
player zero-sum game. The solution is obtained by using the Nash equilibrium of the
game, which is capable of handling a single attacker.
SDR or SDN implementation-based spectrum sensing and sharing facilitate
programmability [14-20], which can be used for the implementation of the neural
network for PUEA detection.
In this work, a neural network-based PUEA detection with energy detection spectrum
sensing is presented. The contributions of this article are the neural network-based
PUEA detection is carried out with real-time Software Defined Radio (SDR)
hardware USRP. A reliable spectrum sensing method is proposed with a lookup table
combined with an energy detector and neural network-based PUEA detection.
The benefit of the proposed approach in the cognitive radio can be summarized as the
proposed reliable spectrum sensing scheme enables detection of PUE attack; thereby
the overall spectral utilization and spectral usage by CR will be improved. The CR
radio will have more opportunity to transmit, and the throughput of individual CR will
be increased. There is a possibility of PU getting interference because of the attacker.
By detecting the attacker by the proposed approach, the attacker and the interference
to the PU are eliminated.
The remaining part of the article is organized as follows: Section 2 gives the system
model and the methodology. Section 3 presents the result and discussion. Section 4
concludes the research work with the future direction of research.
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2. System Model and Methodology
The system model for the investigation is depicted in Figure 1. The system model
consists of three-node, namely Primary User (PU), Primary User Emulation Attacker
(PUEA), and Cognitive Radio (CR), which has a neural network to detect the attacker.
Figure 2 shows the experimental setup used for the implementation of the neural
network-based classifier to identify the PUEA signal. The PXIe chassis with two
vector signal generator and vector signal analyzer is configured as a 2x2 MIMO PU
transmitter and receiver. The first USRP RIO 2943R is configured as a Secondary
User (SU) receiver, which receives signals of both PU signal and PUEA and employs
the neural network classification. The second USRP is configured to perform similar
to a PUEA attacker.
Figure 1. System Model
Figure 2. Experimental Setup
Figure 3. Signal Processing of Cognitive Radio
Primary user
Attacker
Cognitive
radio
RF Front
end Receiver
buffer
BPN
Neural
network
1-Busy
0- Free Reliable
spectrum
sensing
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The signal processing block diagram of the cognitive radio is given in Figure 3. The
RF front end of the USRP hardware receives the signal and performs the conversion
operation internally. And also converts the analog signal into digital data, which are
stored in the receiver buffer. The data from the receiver buffer is used to train the
neural network in the training phase and also managed to classify the data into three
classes, namely primary signal, noise, and attacker signal.
Figure 4. Neural Network Architecture
Figure 4 shows the neural network architecture utilized for the classification problem,
which consists of 4 hidden layers with 15 neurons at each layer having one input layer,
and one output layer. The number neuron in the input layer is equal to the length of
the input feature vector. Considering 300 samples of energy are fed at a time, and the
number of input layer neurons are about 300. This simple architecture is used for less
computational complexity. The sigmoidal activation function is applied to all layers
for computing except the output layer, where the SoftMax layer is utilized for the
three classes.
In this feed-forward network, the ith neuron net valueℎ𝑖𝑘 at kth hidden layer is given
as
ℎ𝑖𝑘 = 𝑏𝑖
𝑘 + ∑ 𝑤𝑗𝑖𝑘𝑡𝑘−1
𝑗=1 𝑜𝑗𝑘−1 𝑓𝑜𝑟 𝑖 = 1,2. . 𝑡𝑘 (1)
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7
Where 𝑏𝑖𝑘 is the bias component of the ithneuron at kth hidden layer;𝑤𝑗𝑖
𝑘 is the weight
vector between the ith and jth neuron at the kth layer ;𝑜𝑗𝑘−1 is the output of input at k-
1th hidden layer from the jth neuron.
The output of the hidden layer 𝑜𝑖𝑘is
𝑜𝑖𝑘 = 𝑎(ℎ𝑖
𝑘)𝑓𝑜𝑟 𝑖 = 1. . 𝑡𝑘 (2)
Where a() referee the activation function operation; ℎ𝑖𝑘 is the net value of ith neuron
at the kth hidden layer
The net value ℎ1𝑚at the output layer and the final output y is
ℎ1𝑚 = 𝑏1
𝑚 + ∑ 𝑤𝑗1𝑘𝑡𝑘−1
𝑗=1 𝑜𝑗𝑘−1 𝑓𝑜𝑟 𝑖 = 1,2. . 𝑡𝑘 (3)
𝑦 = 𝑎(ℎ1𝑚) (4)
Where a( ) is the activation function, 𝑤𝑗1𝑘 is the weight vector between jth and lth
neuron at the kth layer; here, the sigmoidal activation function is used for all layers
used except the output layer, where the softmax layer is accepted for the three classes.
The hypothesis𝜓 to detect the PUEA is given as
𝜓 = {
𝑖𝑓 𝑦 = 1; 𝑃𝑈𝐸𝐴 𝑖𝑓 𝑦 = 0; 𝑃𝑈
𝑖𝑓 𝑦 = 2; 𝑛𝑜𝑖𝑠𝑒 } (5)
The outcome of the PUEA detection is combined with the energy detector spectrum
sensing to obtain the correct decision on spectrum sensing. Since the energy detector
only provides less reliability in the low SNR case, it can be emulated very quickly,
which degrades the performance more comparing other mechanisms. The energy
detector is used as a sensing algorithm. Other methods are not feasible to be emulated
easily, and the chance of an attack is less, and even the attack is present, the
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8
degradation of performance is negligible. The spectrum sensing outcome 𝐷 with the
energy detector is
𝐷 = {0(𝑃𝑈 𝑝𝑟𝑒𝑠𝑒𝑛𝑡); 𝑖𝑓 𝐸𝑡𝑒𝑠𝑡 > 𝑡ℎ 𝑎𝑛𝑑 𝑦 = 0 𝑜𝑟 𝑦 = 1
1(𝑃𝑈 𝑎𝑏𝑠𝑒𝑛𝑡); 𝑖𝑓 𝐸𝑡𝑒𝑠𝑡 < 𝑡ℎ 𝑎𝑛𝑑 𝑦 = 2 𝑜𝑟 𝑦 = 1} (6)
Where 𝐸𝑡𝑒𝑠𝑡 is the average energy calculation in the given frequency, and 𝑡ℎ is the
threshold value used in the energy detector algorithm. An experiment is conducted in
the indoor lab with various transmitting power settings on the PU transmitter; then,
the multiple threshold values are calculated using the median value of the maximum
and minimum energy received in the hardware. The average value of these multiple
measurements is taken as a final threshold value, which can detect the PU even
through PU transmit on various transmit power. The entire process is given as an
algorithm which is given below
Algorithm: Reliable Spectrum Sensing with PUEA Detection using Neural Network
Training phase
Initialization: Make an initial delay of 𝑡𝑑 to make the three SDR to synchronization of transmission and reception and after that make CR SDR acquire data on power trigger mode
Step1: Make PUE attacker send data from a sequenced frequency the set (1.2 GHz to 6 GHz)
with step size increment of frequency 20Mhz from 1.2 GHz to 6GHz.
Step2: Make PU send data from a sequenced frequency in the set (1.2 GHz to 6 GHz) with step
size increment of frequency 20Mhz from 1.2 GHz.
Step3: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step size
increment of frequency 20Mhz.
Step4: Calculate energy from the I/Q sample for all the above-received frequency using a
window size of M=1024 samples as 𝑓𝑝𝑢𝑝𝑢𝑒 =1
𝑀∑ ∑ 𝑠𝑝𝑢𝑝𝑒𝑎(𝑛)𝑒
−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and make this
a feature 𝑓𝑝𝑢𝑝𝑢𝑒set when both PU and PUE attacker presence. Step4: Switch off PU and Make only PUE attacker send data from a sequenced frequency the
set (1.2 GHz to 6 GHz) with step size increment of frequency 20Mhz from 1.2 GHz to 6GHz
Step5: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step size
increment of frequency 20Mhz.
Step 6: Calculate energy from the I/Q sample for all the above-received frequency using a
window size of 1024 samples as 𝑓𝑝𝑢𝑒 =1
𝑀∑ ∑ 𝑠𝑝𝑒𝑎(𝑛)𝑒
−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and make this a feature
𝑓𝑝𝑢𝑒set when PUE attacker only presence.
Step7: Switch off PUE attacker and Make only PU send data from a sequenced frequency the
set (1.2 GHz to 6 GHz) with step size increment of frequency 20Mhz from 1.2 GHz to 6GHz
Step8: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step size
increment of frequency 20Mhz.
Step 9: Calculate energy from the I/Q sample for all the above-received frequency using a
window size of 1024 samples as 𝑓𝑝𝑢 =1
𝑀∑ ∑ 𝑠𝑝𝑢(𝑛)𝑒
−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and make this a feature
𝑓𝑝𝑢set when PUE attacker only the presence
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9
Step10: Switch off both PU and PUE attacker
Step11: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step
size increment of frequency 20Mhz.
Step 12: Calculate energy from the I/Q sample for all the above-received frequency using a
window size of 1024 samples as 𝑓𝑛 =1
𝑀∑ ∑ 𝑤(𝑛)𝑒−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and make this a feature 𝑓𝑛set
when PUE attacker only presence.
Step 13: Train the neural network using the feature set 𝑓𝑝𝑢𝑝𝑢𝑒 , 𝑓𝑝𝑢𝑒 , 𝑓𝑝𝑢 𝑎𝑛𝑑𝑓𝑛
Operational phase
Step1: Make PUE attacker send data by a random frequency from the set (1.2 GHz to 6 GHz)
with step size increment of frequency 20Mhz from 1.2 GHz to 6GHz.
Step2: Make PU send data by a random frequency from the set (1.2 GHz to 6 GHz) with step
size increment of frequency 20Mhz from 1.2 GHz.
Step3: Make SU receive the I/Q data in sequential order from 1.2 GHz to 6 GHz with step size
increment of frequency 20Mhz.
Step4: Calculate energy from the I/Q sample for all the above-received frequency using a
window size of M=1024 samples as 𝐸𝑡𝑒𝑠𝑡 =1
𝑀∑ ∑ 𝑠𝑟𝑎𝑛𝑑𝑜𝑚(𝑛)𝑒
−𝑗𝜔𝑛/𝑀𝑀𝐽=1
𝑀𝑖=1 and apply this
to the neural network for classification and energy detector for reliable detection.
Step5: Using the neural network output y and 𝐸𝑡𝑒𝑠𝑡 the final spectrum sensing decision is given calculated as
𝐷 = {0(𝑃𝑈 𝑝𝑟𝑒𝑠𝑒𝑛𝑡); 𝑖𝑓 𝐸𝑡𝑒𝑠𝑡 > 𝑡ℎ 𝑎𝑛𝑑 𝑦 = 0 𝑜𝑟 𝑦 = 1
1(𝑃𝑈 𝑎𝑏𝑠𝑒𝑛𝑡); 𝑖𝑓 𝐸𝑡𝑒𝑠𝑡 < 𝑡ℎ 𝑎𝑛𝑑 𝑦 = 2 𝑜𝑟 𝑦 = 1}
The final spectrum detection is obtained from the lookup table given below.
Table 1. Final spectrum detection in the presence of PUEA
Energy detector outcome
D
PUEA detection outcome
𝜓 Final spectrum detection
1(PU present) 0(PUEA absent) 0(no spectrum)
1(PU present) 1(PUEA present) 0(no spectrum )
0(PU absent) 1(PUEA present) 1(use spectrum)
0(PU absent) 0 (PUEA absent) 1(use spectrum)
From Table 1, the investigation is based on the PUEA detection energy detector
outcome and the final spectrum detection is made with reliability. Whenever a neural
network detects PUEA, then the energy detector detected it as PU. The lookup table's
final decision will ensure that the SU will get access to the spectrum. So, more
spectrum access will be provided to SU from this scheme.
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3. Result and Discussion
The data needed to train the neural network is captured in real-time using the SDR
hardware with proper experimental setup and realizing the PUEA attack. The SDR
hardware can only generate I/Q samples. At the received side, the received I/Q
samples are obtained from the SDR hardware from which the energy values are
calculated as a feature vector for training the neural network. The experimental setup
for realizing the PUEA and collecting the attacker data is summarized below.
The three USRP, the first one acts as Primary User (PU), the second one acts as
Cognitive Radio(CR), and the third one acts as a PUEA attacker. All the nodes keep
a 10 feet distance from each other. The parameter set used for the experimental study
in real-time using NI USRP 2943R is given in Table 2. PUEA detection is not the
function of the frequency and is based only on the energy level injected by the PUEA.
Many frequency bands are sensed because the attacker may be attempting to attack
various bands dynamically. So, the SDR is programmed to sense many frequency
bands. The attacker SDR generates an attack signal randomly in the range of
frequency band from 1.2 GHz to 6 GHz to simulate a realistic scenario. A dedicated
USRP acts as a PUEA attacker and generates attacker data in real-time. The attacker
USRP SDR is programmed to broadcast the string cautiously “I am PUEA attacker”
in the same frequency (2 GHz) of the primary user uses.
Table 2.SDR parameter setting
Parameter PU Attacker value CR value
Transmit
frequency
2GHz to 6GHz 2GHz to 6GHz 2GHz to 6GHz
Transmit power -10dBm to
+10dBm
10dBm to
+10dBm
10dBm to
+10dBm
I/Q rate 250kbs 250kbs 250kbs
Modulation
scheme
QAM 16 BPSK QAM 32
Sampling rate 10Ms/s 10Ms/s 10Ms/s
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11
Real-time hardware data acquired from the primary user USRP RIO and the PUEA
USRP RIO unit for the training. Ten thousand samples of data from both primary and
PUEA hardware are collected for training, validation, and testing. From the collected
data set, 70% is used for training, 15% is used for validation, and 15% is used for
testing.
To train the neural network, the PU, attacker, and PU plus the attacker need to
manifest the data. Those data are collected by the CR radio using the following
approach.
To collect PU alone training data samples, the attacker USRP kept as an ideal without
transmitting anything, and the PU transmitter alone allowed to send a string “I am
PU” continuously using the parameter setting in Table 2. The collected string is
decoded before storing the data sample to ensure that the data gathered solely from
PU. Similarly, to obtain the attacker alone, transmitting data is received by keeping
the PU ideal and making attackers continuously send the “I am PUEA attacker” string
in the same frequency of PU. This label is decoded before storing the collected data
to ensure the data collected is valid data from only PUEA attackers. The PU and
attacker are enabled to transmit the “PU and PUEA “string continuously to capture
both present case data.
The transmit power of the USRP is varied from -10 dBm to +10dBm in a step size of
5dBm to collect data for the above three cases. This arrangement captures the
variability of the wireless channel.
As another variability, the transmit frequency is changed from 2GHz to 6GHz in a
step size of 0.5 GHz and the three cases of PU alone, PUAK attacker alone, and both
present.
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Figure 5.MSE value Vs. Epochs
The Mean Square Error-values for various training, validation, and testing iteration
are plotted in Figure 5 to observe the training performance. It is observed from the
above figure, that the data set after 53 Epochs and the neural network are converged
successfully to classify the PU and PUEA data with minimum MSE criteria.
Figure 6. Training state
0 10 20 30 40 50
10-2
10-1
100
Best Validation Performance is 0.025011 at epoch 53
Mea
n S
qu
ared
Err
or
(mse
)
59 Epochs
Train
Validation
Test
Best
10-3
10-2
10-1
100
grad
ient
Gradient = 0.004842, at epoch 59
0 10 20 30 40 500
2
4
6
val f
ail
59 Epochs
Validation Checks = 6, at epoch 59
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13
The training state of the neural network is observed from the gradient and validation
checks and it is depicted in Figure 6. The figure proves that training is carried out well
in the minimum gradient direction, and the validation checks. It also confirms that the
trained neural network fails to classify only for a few samples in validation.
Figure 7. Error Histogram
The error histogram values are plotted in Figure 7, which illustrates the distribution
of error values during the training, validation, and testing stage. And also records that
a large volume of the data samples is getting near to zero error. It confirms that the
training of the neural network is done accurately.
The training confusion matrix for the classification problem is computed and given in
Figure 8. The classification accuracy and error of the trained neural network are
attained. The diagonal element shows the correct classification for the given problem
and it is observed that 96.7% correct classification is carried on the training phase,
97.3% on validation, 97.8% in the testing phase, and 96.9% in total.
0
100
200
300
400
500
600
700
800
900
1000
Error Histogram with 20 Bins
Inst
ance
s
Errors = Targets - Outputs
-0
.605
1
-0.5
232
-0.4
413
-0.3
595
-0.2
776
-0.1
957
-0.1
139
-0.0
32
0.04
987
0.13
17
0.21
36
0.29
55
0.37
73
0.45
92
0.54
11
0.62
29
0.70
48
0.78
67
0.86
86
0.95
04
Training
Validation
Test
Zero Error
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14
Figure 8. Confusion Matrix
The diagonal element of figure 8 shows the correct classification for the given
problem and it is observed that 97.1% correct classification is carried on the training
phase, 95.7% on validation, 100% in the testing phase, and 97.3% in total. The off-
diagonal elements show the percentage of the missed classification, which is around
0.7% and 2% in the total case. The above confusion matrix values prove that the neural
network will able to classify the radio as PU and PUEA more accurately.
Figure 9. Receiver Operating Curve
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15
Receiver Operating Curve (ROC) for the training phase, validation phase, test phase,
and combined all phase results are plotted in Figure 9.ROC gives a trade-off between
two performance measures of classifiers called sensitivity and specificity. In the ROC
curve, the false positive rate is on the x-axis, which represents 1 – specificity. The true
positive rate is on the y-axis, which represents 1-sensitivity.
Specificity gives the performance measure of the whole negative part of the data set.
Sensitivity provides performance measures of the entire positive part of the dataset. If
the performance curve is diagonal, then the classifier performance is poor because it
favors both sides of the data equally. If the curve is on the top left side and above the
middle diagonal line, then the performance is good. The ROC curve below the middle
diagonal line shows poor performance.
For the perfect classifier, the ROC curve should be aligned with the x and y-axis.
Figure 9 ROC curve shows that the performance of the classifier is perfect in all three
stages called training, validation, and test. From all ROC curves in the above-
mentioned figure proves that the classifier performs perfectly because the curves align
with the x and y-axis at the top left side above the middle diagonal line.
Figure 10. Probability of Detection
-20 -15 -10 -5 0 5 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SNR
Pro
bab
ilit
y o
f D
etec
tio
n
proposed work
GWOA
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16
The PUE attack detection output is combined with an energy detector sensing
algorithm is to calculate the probability of detection and to validate the proposed
method which is described in Figure 10. The graph gives the average value of the
probability of detection, which is then computed over 50 trials. SNR calculation in
real-time SDR is calculated with the help of Error Vector Magnitude (EVM). The
EVM vector can be extracted from the SDR hardware from which the SNR is
calculated using the following relationship S𝑁𝑅 =1
𝐸𝑉𝑀2. The obtained final result is
compared with the similar work of a hybrid Genetic Whale Optimization Algorithm
(GWOA)[21]. Figure 10 proves that the proposed neural network-based PUEA
detection and spectrum detection outperform when comparing with the literature
work. While the GWOA carries 5dB SNR to achieve probability detection of 1, the
proposed scheme can attain it around 2.5dB. This result archives 2.5dB SNR gain
comparing to that of the literature method to accomplish probability detection of
100%.
4. Conclusion and Future Scope
Detection of primary user emulation attack signal is the biggest challenge in the real-
time wireless channel, which leads to accomplishing rapid changes in the signal
characteristics of both primary user and primary user emulation attack. This proposed
work handled this issue effectively by using neural networks. The problem of
detecting the primary user emulation attack is formulated as a two-class classification
problem to classify the signal into the primary user and primary user emulation attack.
The neural network is designed using four hidden layers with 15 neurons, one input,
and one output layer. The system setup is carried out with three universal software
radio peripherals. The Software-Defined Radio is to represent the primary user,
primary user emulation attack, and cognitive radio with 10 feet from each other. The
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17
data collection and training is carried out with different frequency and transmit power.
The receiver operating curves-based validation of the trained model is accomplished.
The real-time signal classification is achieved with an accuracy of 97%, which enables
to use of the proposed approach in the real-time system deployment. The outcome of
the neural network is applied to the energy detector algorithm, and the probability
spectrum hole detection is also executed. The proposed scheme is also compared with
literature, and it is proved that the proposed method outperforms with 2.5dB gain for
a 100% probability of detection. The detection time is one of the primary factors in
cognitive radio, especially for multiband sensing. The future work will focus on
reducing the decision time by optimizing the proposed system.
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Vijayakumar Ponnusamy completed Ph.D. from SRM IST, M.E from Anna University and B.E from
Madras University. He is a Certified “IoT specialist” and “Data Scientist.”His current research interests
are Machine Learning, Deep Learning, IoT - intelligent system, and cognitive radio. He is currently
working as Associate Professor in the ECE Department, SRM IST, Chennai, India.
KottilingamKottursamy is an Associate Professor in the School of Computing, SRMIST, India. He
has completed his P.hd from Anna University. He is one of the Co-Principal Investigators of IITK-
SPARC Project, in collaboration with UC Davis, USA. A former Member NGNLab, Anna University,
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19
Chennai, India. His research interest includes Artificial Intelligence, Next Generation Networks, and
Bio-Statistics.
T. Karthick is Assistant Professor in SRMIST, Chennai, India. He holds a Ph.D. Degree from Anna
University and M.Tech from Sathyabama University. He has 11 years of teaching and 5- years of
industry experience. His research areas include IoT, Cloud Computing, Machine learning. He has
published more than 15 Papers in Journal and attended 10 International and national conferences.
M.B. Mukesh Krishnan received his Ph.D. in Computer Science and Engineering, Currently working
as Associate Professor in Department of Information Technology, SRM Institute of Science and
Technology, Chennai, India. His research interest includes Wireless Sensor Networks, Under Water
Sensor Networks, Mobile Computing, Ad hoc networks, Mobile Ad hoc Networks, and Network
Security.
D. Malathi is a Professor in the Department of Electronics and Communication Engineering, Kongu
Engineering College, Perundurai, India. She obtained her B.E degree from Madras University, ME
from Anna University, and a Ph.D. degree from Anna University, Chennai, India. She had published
more than 40 papers in conferences and Journals. Her area of interest includes Low Power VLSI Signal
Processing.
Tariq Ahamed Ahanger is currently an Associate Professor with the College of Computer
Engineering and Sciences, Prince Sattam Bin Abdulaziz University. His interests include the Internet
of Things, cybersecurity, and artificial intelligence.
-
CONFLICT OF INTERST
The authors of this manuscript does not have any conflict of interest.
Conflict of Interest
-
Author Statement
The author contributions are:
Vijayakumar Ponnusamy: Conceptualization; Data creation, ; Investigation, Methodology; original
draft, Validation; Visualization, Writing - review &editing, Software
Kottilingam K: Funding acquisition, Formal analysis, Writing - review &Editing; Visualization, Project
administration, Resources
Karthick. T: Funding acquisition, Formal analysis, Writing - review &Editing; Visualization, Resources
M.B. Mukeshkrishnan: Formal analysis, Software; Writing - review &Editing; Visualization
D.Malathi, Professor- Writing - review & editing, Formal analysis, Visualization, Resources
Tariq Ahamed Ahanger: Formal analysis, Software; Writing - review &Editing, Visualization;
Resources
Author Statement