Computers and Electrical Engineering...NEURAL NETWORK” for the consideration to publish in the International Journal of Computers & Electrical Engineering. I confirm that this work

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 You must use a native English-speaking editor.Papers with less than excellent English will not be published even if technically perfect.Examples:- Abstract: "Primary User Emulation Attack (PUEA) is physical layer attack in, which disables the cognitive radiouse the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

1

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

2

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]

3

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

4

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

5

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.

6

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.

7

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

8

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

9

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)

10

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

11



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


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




using a window size of 1024 samples as 𝑓𝑝𝑢 =1

𝑀∑ ∑ 𝑠𝑝𝑢(𝑛)𝑒



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.


using a window size of 1024 samples as 𝑓𝑛 =1

𝑀∑ ∑ 𝑤(𝑛)𝑒−𝑗𝜔𝑛/𝑀𝑀𝐽=1


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)


Step2: Make PU send data by a random frequency from the set (1.2 GHz to 6 GHz) with step




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


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


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

12

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

13

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.

14

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

15

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

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Error Histogram with 20 Bins

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Errors = Targets - Outputs

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16

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.

17

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

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

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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

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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transactions on wireless communications, 14(9), pp.4981-4993.

6. Ta, D.T., Nguyen-Thanh, N., Maillé, P. and Nguyen, V.T., 2018. Strategic surveillance against primary user emulation attacks in cognitive radio

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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

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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


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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& Computing, Advanced & Trusted Computed, Scalable Computing &

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Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1-7).

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Techniques for Advanced Computing Applications (pp. 167-178). Springer,

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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

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

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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2

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

3

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.

4

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.

5

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

6

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)

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

8

degradation of performance is negligible. The spectrum sensing outcome 𝐷 with the

energy detector is


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)


Step2: Make PU send data from a sequenced frequency in the set (1.2 GHz to 6 GHz) with step




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


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



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


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




window size of 1024 samples as 𝑓𝑝𝑢 =1

𝑀∑ ∑ 𝑠𝑝𝑢(𝑛)𝑒


𝑀𝑖=1 and make this a feature

𝑓𝑝𝑢set when PUE attacker only the presence

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.


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)


Step2: Make PU send data by a random frequency from the set (1.2 GHz to 6 GHz) with step




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


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


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.

10

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

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.

12

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

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

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

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

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

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,

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

Documents

Computers and Electrical Engineering...NEURAL NETWORK” for the consideration to publish in the International Journal of Computers & Electrical Engineering. I confirm that this work