Defense against PUE Attacks in DSA Networks
using GAN based Learning
Debashri Roy∗ , Tathagata Mukherjee† , Mainak Chatterjee∗ , Eduardo Pasiliao‡∗ Computer Science† Computer Science‡ Munitions Directorate

University of Central Florida
Orlando, FL 32826{debashri, mainak}@cs.ucf.edu

University of Alabama
Huntsville, AL 35899{tm0130}@uah.edu

Air Force Research Laboratory
Eglin AFB, FL, 32542{eduardo.pasiliao}@us.af.mil

Abstract—Primary user emulation (PUE) attacks can pose asignificant threat to the deployment of a robust cognitive radio
network implementing dynamic spectrum access, for an intelligent allocation and usage of already crowded spectrum bands.
In this paper, we present a solution towards the PUE attacks.
We present two generative adversarial net (GAN) based models
to successfully emulate the primary users (PUs) in two ways.
We propose a (i) dumb generator model without any “prior”
knowledge of PU’s feature space, (ii) a smart generator modelwith some “prior” knowledge about PU’s transmission. We also
propose two deep neural network based discriminator models todiscriminate between the PU and the emulated primary users(EPU) from the corresponding generators. Both the generator
and discriminator of each GAN model gets smarter with iterativeand sequential GAN training. Through a testbed evaluation,
we show that discriminators are able to catch ∼50% of PUE
attackers without the GAN training during the deployment phase.
We also observe 100% accuracy for both the GAN models
during training phase. Ultimately, after the GAN training, thediscriminators achieved 98% and 99.5% accuracies, for dumband smart generator models respectively, to distinguish “yet to
be seen” PUE attacker.

Keywords: PUE Attack, generative adversarial nets, deepneural network, software defined radios, confusion matrix.
I. I NTRODUCTION
The opportunity for abundant usage of wireless devices
has created an overly crowded radio spectrum and led to thescarcity in spectrum availability. In order to deal with thisdifferent pervasive measures are taken to deal with competitive
nature of the spectrum availability. However, studies haveshown that a large portion of licensed spectrum is unused
at any given time or location [1]. To exploit such unused
spectrum, the concept of dynamic spectrum access (DSA) was
envisioned. The deployment of such spectrum management
approach requires the use of intelligent systems at lower levelsof the communication stack, even at the end users. Cognitive
radio networks (CRN) have proven its competence for such
deployments [2]. The basic idea of DSA is to allow some
unlicensed users (secondary users) to opportunistically accessthe spectrum of the licensed users (primary users) when the
spectrum is not in use. The rules strictly restrict any harmful
overlap or pretentious use of spectrum by secondary users(SUs) when primary users (PUs) are present. The cognitive
properties of CRN, enable the radio devices to take decisionon how to manage spectrum for both PUs and SUs. One

important challenge in order to achieve the goal of ideal CRN
deployment is ensuring the security of PUs.
Most of the research on such spectrum allocation andmanagement techniques for DSA deployment, are build on
the assumption that all participants are cooperative, honest,
and the network is without the presence of adversaries. The
Federal Communications Commission (FCC) [3] mandated
that all SUs must release the occupied spectral band as soon
as any PU starts to transmit in that band, ensuring full privacy
and availability for the licensed users. However, since till now
the CRN network deployments are lacking any measure to
implement the security guidelines [4], a situation could arise
where the PUs get denied the required spectrum due to thepresence of a malicious SU. This threat could be categorized
as denial-of service (DoS) attack [5]. An adversarial SU could
pose itself as a PU by transmitting the signal with characteristics identical to the PU. Such malicious SUs could threaten
the integrity of the CRN in two ways: (i) by preempting
the existing SUs in any spectral band, by posing as a PU;
and (ii) by fooling the spectrum manager to deny the PU, as
the malicious SU is impersonating itself as a valid PU. Suchmalicious deployment of large scale can hijack the entire whitespace of any spectrum band, thus launching a “DoS” attack on
the legitimate SUs and PUs. Such attacks were first describedby Chen et al. [6] as primary user emulation (PUE) attacks.
In this paper, we propose a machine learning based technique to detect PUE attacks. We design and implement a
generative adversarial network (GAN) [7] based approach togenerate malicious SUs. Any GAN based model works on
a synergistic training of two neural networks: (i) a generator,and (ii) a discriminator. Therefore, we use generated malicious
entities to train the discriminator for “yet to be seen” real PUE
attackers. The main contributions of this paper are:1) We impersonate SUs using two types of generator
models: (a) dumb attacker, and (ii) smart attacker. The
dumb attacker has no “prior” information about thesignal characteristics of the PUs, but still tries to emulate
the PUs. However, the smart attackers has sufficient
information about the PU’s signal data and therefore canemulate the PU’s signal in an intelligent way.
2) We also model two discriminators (neural networks) andtrain them over the PU data, and generated data. The“dumb discriminator” gets generated data from dumb978-1-7281-0962-6/19/$31.00 ©2019 IEEE

generator attack model, and “smart discriminator” getsdata from smart one. We show that the GAN training
makes the discriminator able to distinguish between
a wide array of possible malicious entity types andtherefore being able to detect the real adversaries withintention of PUE attack.
3) Using USRP, we collect the raw over-the-air signal datafrom PUs to train both the discriminators. We also
extract the PU’s signal characteristics from the collected
data and train the smart generator.
4) We show that the untrained discriminators have a ∼50%
accuracy for detecting PUE attackers during the deployment phase.
5) We present 100% training accuracy for both the generator models. The trained discriminators over dumb, andsmart generator models exhibit testing accuracy of 98%,
and 99.5% respectively during deployment phase, withreal PUE attackers present.
Next we present the background information and related
works on PUE attacks.
II. BACKGROUND AND R ELATED W ORKS
In this section we discuss the main premise of PUE attackand its properties. We also present different ideas for defending
against these attacks and finally we discuss the existing
research on these concepts.
A. PUE Attack
PUE attack, first conceptualized and proposed by Chen etal. [6], is a DoS attack for CRNs. In a CRN, the PUs areprioritized over any SU. At the heart of a CRN, a spectrum
manager makes the decision of preempting SUs when any
PU is in need of transmission. For example, the PUs can be
TV stations with a wide range, wireless microphones with alimited range [8], or mobile public safety devices surging withsudden transmissions during the times of emergency [9]. The
SUs can be conceptualized as wireless devices connected to a
WiFi network.
As per the policies mandated by FCC [3], SUs cannot cause
any interference with PU’s transmission, or hurt the PU’stransmission in any other way. However, the malicious SUs
can pretend to be a PU by “mimicking” certain features ofPUs. One example is, where a malicious SU emulates itself
to be a PU by using a low power commercial off-the-shelf
TV transmitter [10] located near some legitimate PUs andstarts to transmit on a particular spectrum band. The objectiveof the emulated primary user (EPU) could be of two types:(i) a selfish goal to maximize the spectrum usage for itself,(b) malicious goal with a tendency to prevent the legitimate
SUs from detecting vacant spectrum bands, leading to a DoS
attack. The selfish EPU attacker starts transmitting on a vacant
frequency band without going into the waiting queue for SU
selection by the central spectrum allocator. The malicious EPU
attacker starts to “attack” over multiple vacant frequency bands
randomly, resulting in starvation of PUs’ and legitimate SUs.

B. Existing Defenses against PUE Attack
Any security threat can be thwarted by several defense
mechanisms through continuous research. Though there is stilla dearth of opportunities to come up with a robust defense
mechanism to overcome all technical challenges related to
PUE attacks, there are few existing defense mechanisms
which are available today. One such technique is matchedfiltering-based detection [11] of PUE attacker, which requires
specialized hardware and software. On the other hand, energy
detection-based schemes [12] poises high risk of missed false
alarm and missed detection possibility. The location-based
detection technique [6] is limited to stationary PUs with known
coordinates. Another approach was the use of phase noise as asignature [13] to identify PUs and defend against PUE attacks.
The key idea behind this technique is to erase modulation from
captured signal (which is modulated) and extract phase noiseof local oscillator (LO) to work as a signature. This approachis also constrained over the prior knowledge of modulation
scheme. However, the use of a cryptographic signature andwireless link signature [14] to detect PUE attacker requires anadditional helper node in close physical proximity of each PU.
Similarly, a cyclostationary calculation [15] based artificial
neural network model needs prior knowledge of modulation
schemes of PUs, and it is constrained to be different for theother users.
All the mentioned defense mechanisms are burdened withoverheads or constraints of different types. To resolve this, we
propose a GAN based robust PUE attack defense which will
involve only centralized deployment and can provide defense
mechanism for both mobile and stationary PUs or legitimate
SUs, without any such constraints.
III. P ROPOSED GAN A PPROACH
In this section we propose a GAN based approach to present
a robust defense mechanism for PUE attack. In a GAN, wehave one generator and one discriminator. We propose two
GAN based models: (i) one model which will work without
any prior knowledge about the PU or the CRN, (ii) theother with the assumption, that the attacker will have priorinformation about the PUs characteristics for that CRN. So,
for the first case we take the SU to work as a dumb emulated
primary user (EPU). However, in the second case the EPU issmart to gather or know all the helpful prior information about
the PU transmission, so we call those as smart EPUs. Few
examples of prior knowledge that an attacker could leverage
to mimic the PUs characteristics are: (i) a set of modulation
schemes which the PUs use, (ii) the used frequency band, (iii)channel bandwidth for PU’s transmission, (iv) geographicallocation of the PU’s transmitter, (v) sample space and sample
size of the PU transmission, and so on.
A. GAN based Problem Formulation for PUEA Defense
A GAN framework comprises of two distinct models: thegenerator (G) which learns the real data distribution andgenerates the “mimicked” data, and the discriminator (D)which tries to distinguish the mimicked data from the “real”

data by estimating the probability that the sample came fromthe real data rather than the G. The overall idea is that, during
the training phase the generators will pose as a selfish or
malicious PUE attacker and make the discriminator stronger
in order to fight against the real selfish and malicious PUE
attackers during the deployment phase. We call the selfish andmalicious PUE attackers as emulated primary users (EPU),
and the legitimate primary users as PUs. We use a centralized
spectrum allocator and train it using the GAN model andtherefore work as a robust discriminator during deployment.
a) Generative Model: The generative model has two
main inputs, (i) prior information about the PU’s feature space(s(t)), (ii) a additive Gaussian white noise (n(t)). The noiseis added with the prior information, z(t) = s(t) + n(t). The
output z(t) is then fed to the generator which works as aunsupervised learning tool and learns the data distribution(pz (z)) of PU’s feature space. Note that we have indexed
the prior information, noise and the generated data by timet. This has been done to acknowledge the fact that the signal
characteristics can change over time. However for this work
we do not consider the effects of variation of the signal
characteristics (prior information) and noise with time andessentially assume that s(t) = s ∀t and n(t) = n ∀t. Hence
we also get z(t) = z ∀t. Recall, there are two types of EPUs,
one is dumb and another smart. The generator for dumb EPU
is deprived of any prior information about the PU’s feature
space, so s(t) for the generator model of dumb EPU is random
and does not reflect the actual signal characteristics of the PU.
The cost function of generator is denoted by V (G). The GAN
model’s target is to minimize this cost, i.e., to minimize theprobability of D correctly identifying the data from G.
b) Discriminative Model: The discriminator is fed withboth real data (x) drawn from a data distribution pdata (x),and generated data from generator (z(t)). The objective ofthe discriminator is to successfully distinguish between these
two types, i.e., to learn the difference between pdata (x), andpz (z). The cost function for discriminator is denoted as V (D).
The target of discriminator is to maximize its cost, i.e., toincrease probability to correctly identify samples from training
examples and data from G. Target of the overall GAN model
is to minimize the cost for generator and maximize the costfor discriminator. The overall cost V (G, D) is formulated
as V (G, D) = Epdata (x) log D(x) + Epz (z) log(1 − D(G(z))),where pz (z) is the generator’s distribution over generated data
samples z, pdata (x) is the data distribution over real data
samples x, D(x) is the probability that x came from pdata (x)than pz (z). D(G(z)) represents the probability that x camefrom pz (z) than pdata (x). Objective of the GAN training is:
min max V (G, D)
G

D

(1)

Throughout the training phase, the GAN framework eventually converges to an unique optimal discriminator for a particpdata (x)ular generator, D∗ (x) =. It is intuitive thatpdata (x) + pz (z)D is optimal when the discriminator can distinguish between
each real sample (x) and generated sample (z). Similarly, it isalso deduced that G is optimal when the D cannot distinguishbetween x and z, G is optimal when pz (z) = pdata (x).
min max V(D,G)
G

D

Radio
Real Data

Discriminator
NetworkD(x)x~pdata(x)Priorz~pz(z)Generator
NetworkG(z)Generated
Data

Activation
Function

Real
Fake

Fig. 1. Implementation of GAN in RF Domainc) Proposed GAN Architecture: The overall GAN architecture is shown in Fig. 1. Once the generator (G(z)) is trained,
it generates “mimicked” data from the data distribution (pz (z))
with the prior information. The trained discriminator knows
the difference between the mimicked data distribution (pz (z))
and real data distribution (pdata (x)), so it will try to distinguishthe mimicked data (z) from the real data (x). The activation
function at discriminator is sigmoid. The feedback from theoutput is fed to both G(z), and D(z). So ultimately the targetof overall GAN model is to maximize the discriminator’s costand minimize the generators cost, mathematically formulated
as equation (1).
Training Phase
Specturm Allocator
PU FeatureSpace

TuningParameterss(t)Generator

Noise

z(t)

Discriminatorn(t)Raw Signal
Data

Is Real?

Real
Data

PU Feature
Space
Extractor
Wireless links
PU1

PU2

PUn

Wired links

Fig. 2. GAN Training in the Spectrum Allocator

B. Proposed GAN based Approach for PUEA Defense
The proposed method is compatible with the existing system of centralized spectrum allocation for dynamic spectrum
access implementations. The idea is to install a generator anddiscriminator model inside the spectrum allocator. During thetraining phase, the discriminator is trained over the generated
data and real signal data from the legitimate PUs. The overall
proposed training approach is presented in Fig. 2. We train
the discriminator over the generator model. We consider thegenerator to be either one of the two types: (i) a generator
posing as a dumb EPU, (ii) a generator posing as a smart
EPU. The smart EPUs collect the prior information about thefeature space of the PUs from a feature space extractor. The
discriminator gets trained over both real (x) and generated
data (z) over iterations and become robust enough to distinguish between the real and synthetic data. The output of thediscriminator is fed to both the generator and discriminatormodels so as to tune-up the hyper-parameters of each model,

PU1

PU2

PU Feature
Space
Extractor

SU1
Generator
Smart EPU

SU1

Generator
EPU
Wireless links

PU3
Noise

Noise

Wired links

Fig. 3. Deployment of Trained Discriminator in Spectrum Sensing Scenario

During the deployment phase, the centralized spectrum allocator gets signal information from spectrum sensors, and passthem through the trained discriminator before the spectrum isallocated. The overall proposed approach for deploying thetrained discriminator is shown in Fig. 3. In the figure we
consider 3 legitimate PUs, 1 dumb EPU, 1 smart EPU, and2 SUs. The dumb EPU does not have any prior knowledge
about PU’s feature space, but the smart one has. The trained
discriminator is able to recognize the 2 EPUs as SU, despite
the effort of the malicious entities.
IV. I MPLEMENTATION AND R ESULTS
For implementing the GAN model, we use the proposed
generator and discriminator models with data collected fromover-the-air RF transmission. Next we describe the datacollection, experimental setup, implementation details, andexperimental results.

TuningParameters

Dense

N

N/2

N

2N

Fake
Data
Trusted
Data

Dense

Noise

Dense

We use a dataset of raw I/Q values from 8 software defined radios (universal software radio peripheral (USRP) B210[16]). The details of I/Q data generation and data collection
mechanism is similar to [17], and [18]. Each dataset comprises
T training samples (for T timestamps) and a sample size of
N , where each sample is a vector (I, Q) ∈ C representing
a number in the complex plane. Each vector at timestamp t
is represented as: x(t) = [[(I, Q)i ]t ; i = 1, 2, · · · , N ] ∈ C N
for timestamp t = 1, 2, · · · , T . We use these vectors as input
during GAN training. Note that though we collect the datawith respect to different time stamps, we do not treat the datathus collected as a time series for this work, that is we ignore
the temporal aspect of the data.
We conducted the testbed evaluation on a Ryzen 8 Coresystem with 64 GB RAM, a GTX 1080 Ti GPU unit, and 11
GB memory. We used different machine learning libraries todesign the proposed GAN models. We useu Keras [19] as thefrontend and Tensorflow [20] as the backend for the neuralnetwork architectures used in GAN. We also use Numpy,

Random

A. Used Dataset and Experimental Setup

Data fromPrimary Users[0, 1] OR [1, 0]

2N

N

N/2

2

Output

3 PU
4 SU

Dense

Results

TrainedDiscriminator

In this case, the generator has no prior information. The
generator starts by randomly generating data within the sample
space [X, Y ], where X and Y are random numbers. For our
experiments, we took X as 0 and Y as 1. This dumb generator
picks a random sample size N . The randomly generated datais then used as input to three dense layers of sizes N/2, N , and
2N respectively with tanh activation function in the first twolayers. The third neuron layer uses sigmoid activation function,and generates data of size 2N .
The generator G(z) continues to learn the data distribution(pz (z)) and generates fake samples of size 2N within arandom sample space. The discriminator (D(x)) for the dumb
generator is simple. We first have one input layer of 2N nodes,
which is followed by two hidden layers of N and N/2 nodes
respectively. Ultimately, a softmax output layer of 2 nodes isused to identify whether the input is from a legitimate SUor malicious EPU. We use tanh as activation function at thehidden layers and add Dropout [21] of 0.5 in between thoselayers to avoid overfitting. We also use l1 regularization forthe same purpose. The output from the last layer is fed to
the layers of generator as well as discriminator in the next
epoch for tuning the parameters of both. One epoch is actually
a combination of a forward and a backward pass throughthe designed model over the entire dataset. The overall GAN
implementation using dumb generator is shown in Fig. 4. In
this case, the G(z), and D(x) are respectively 3-layered and5-layered networks. In the figure, the solid lines represent
the connection between two layers, whereas the dotted lines
represent the feedback from output layer of the discriminatorto the other layers of both generator and discriminator to be
used in next epoch.

Dense

Spectrum Allocator

B. GAN Model for Dumb PUE Attacker

Dense

Deployment Phase

Scipy, and Matplotlib python libraries for implementation ofdifferent operations.

Dense

depending on the result. In this way, the generator also getssmarter over each iteration and the discriminator gets smarter
than the generator over the next iteration. Thus we design
the training in such a way that over time the discriminator
eventually over-powers the smartest possible generator model.
Once the GAN training is complete, the trained discriminator
is deployed in the spectrum allocator.

2

Fig. 4. GAN Implementation for Dumb Emulated Primary User

C. GAN Model for Smart PUE Attacker
In this case, generator G(z) has prior knowledge about somefeatures of PUs. We get a sample of size 1024 and sample
space of [-1, 1] from the PU feature extractor. These features
eventually help the EPUs to better mimic legitimate PUs.
In this set of experiments, we get the sample size (N ) andthe sample space from the “feature space extractor”. Feature
space extractor is a module in the spectrum allocator, which
extracts features and builds a feature space for the PUs fromthe sensed signal data. The GAN implementation with a smart
generator is shown in Fig. 5. For the sake of consistency andgenerality, we use the same number of layers as the dumb one,

for modeling the generator of smart EPU. However, knowingthat the generator is smart, we design the discriminator in a
way that it is 1-layer more deep than the one for the dumbone. We use 2 dense layers with N nodes instead of 1. So
now the D(x) becomes a 6-layered network. The rest of thegenerator and discriminator properties are similar as before.

Feature Space
Extractor

Dense

2N

N

N

N/2

2

Output

Dense

2N

Dense

N

Dense

Dense

N/2

[0, 1] OR [1, 0]

Dense

Dense

N

Fake
Data
Trusted
Data

Dense

Feature
Space

Noise

RandomTuningParameters

2

Data fromPrimary Users

Fig. 5. GAN Implementation for Smart Emulated Primary User

D. Experimental Results
We perform two sets of experiments here: (i) first we train
the GAN model on a dumb generator and use the discriminator
as discussed in section IV-B, (ii) next we train our proposedmodel on the smart generator and use a discriminator as
described in section IV-C. For both the cases we performed
the training on the data collected from the authentic PUs. We
notice that the discriminator trained on the dumb generator
was able to detect the EPUs with 49.22% accuracy before the
GAN training. Whereas, the discriminator trained on smart
generator was able to do that with 50.15% accuracy. This slight
improvement is due to the one extra layer of neurons in thesmart discriminator. However, the overall achieved accuracy
is far below expectations. The discriminator is naive andcan distinguish the EPUs from the legitimate PUs only with
∼ 50% of accuracy, which is similar to what one would obtainfor random guessing.
1) Training Phase: We train both the generator and discriminator through iterative sequential learning to strengthen
the generative and discriminative model over time. We use
categorical cross-entropy training on Adam [22] optimizer forgradient based optimization. We use 90%, 5%, and 5% of thetotal data for training, cross-validation and testing respectively.

(a) 50 Epochs(b) 100 Epochs(c) 150 Epochs

learning rates by 1/10 again. Notice that in each epoch, thelearning rate of generator is lower than the discriminator, as
we want the generator to learn precisely about the possible
data distribution (pz (z)) of PUs for better EPU emulation.
However, we give the discriminator more layers to be able toaccurately learn the decision boundary in order to eventually
overpower the generator. It is clear from the Fig. 6, that thegenerator’s loss starts to decrease and the discriminator’s loss
starts to increase at the beginning. However, after a certain
number of epochs, both the losses saturate. The generatorloss, with the dumb generator fluctuates more and reaches
saturation later (after 30 epochs); whereas the generator-loss
with smart generator reaches saturation within less than 20
epochs, as shown in Fig.7. This result bolsters the intuition thatknowing some of the PUs features will make the generators
smart at emulating the PUs and this in turn will make thesystem converge faster. The discriminator’s behavior is moreor less stable for both the models. Once both the models aretrained, we proceed to the deployment phase.

(a) 50 Epochs(b) 100 Epochs(c) 150 Epochs

Fig. 7. Generator and Discriminator Loss of GAN Model with Smart EPU

2) Deployment Phase: During the deployment phase, we
randomly choose over-the-air signal data from EPUs and SUs.
We collect 4000 signal data from legitimate SUs and 4000
signal data from EPUs. We programmed USRP B210s [16] towork as SUs and EPUs. These EPUs use same sample sizeand sample space as the PUs, making themselves indistinguishable to the normal discriminator (trained without GAN).
We observe ∼ 50% EPU detection rate prior to GAN training.
However, after the discriminator is trained and learned the data
distribution (pz (z)) of the EPUs, we get better detection rate.
We achieved a 98.04% accuracy from the discriminator which
was trained using the dumb generator and 99.5% accuracy
from the discriminator which was trained using the smart
generator. These results are presented in Table I.
D with Dumb G Training
D with Smart G Training

Before GAN Training
49.22%
50.15%

After GAN Training
98.04%
99.5%

Fig. 6. Generator and Discriminator Loss of GAN Model with Dumb EPU

TABLE I
ACCURACIES FOR D IFFERENT I MPLEMENTATIONS

We experimented with several training paradigms for training the GAN models in order to get the best performancefrom it. This leads us to train both the models in a 3step approach. We first train the models for 50 epochs withlearning rate of 10−4 and 10−3 for generator and discriminator
respectively. Next, we train the models for another 50 epochswith lower learning rates of 10−5 and 10−4 for G(z) and D(x)respectively. In the last 50 epochs, we decrement both the3) Performance Analysis: Using accuracy as a measure ofefficacy of an algorithm can sometimes be incomplete andmisleading depending on the type of data, such as the case forskewed data. A confusion matrix overcomes those problemsby showing a relative relation between false positives andfalse negatives and actual data labels. Hence we present theconfusion matrices for both the experiments of deployment
phase, in Fig. 8, and 9. It is evident from Fig. 8, that almostduring the deployment phase. However, GAN model withsmart generator training achieves better accuracies and fastersaturation than the dumb one. Both models are able to detect
the malicious and selfish PUE attacker with more than 98%
of accuracy. Extending this concept towards providing security
for other issues in wireless communication, could be one of
the next steps to consider.
R EFERENCES(a) Dumb Generator(b) Smart Generator

Fig. 8. Confusion Matrices with Dumb and Smart EPUs: before GAN
Training(a) Dumb Generator(b) Smart Generator

Fig. 9. Confusion Matrices with Dumb and Smart EPUs at Deployment Phase:after GAN Training

all of the signals are predicted to be transmitted from SU,
giving an accuracy of ∼50%. So, the discriminator is not able
to distinguish between EPU and SU, thus predicting all as SU.
However, the confusion matrices after the GAN training has
lower false positive and false negative rates. We also noticethat the false positives and false negatives are higher in caseof the GAN model which was trained over dumb generator.
In summary,• We achieve ∼50% accuracy of EPU detection using thediscriminator trained without the GAN for both type of
proposed models, during deployment phase.
• During training, we observe 100% training accuracy forboth dumb and smart GAN models.
• The trained discriminator with dumb and smart generator training gives testing accuracy of 98% and 99.5%
respectively during the deployment phase.
• The proposed GAN based model can be applied for any
type of cognitive radio transmitter irrespective of PU or
SU’s properties. We achieve 98-99% accuracy of EPU
detection after the dumb generator training. So without
any knowledge of PU properties, the proposed model iscapable of detecting PUE attackers with 98% accuracy.
Some “prior” information can boost up the accuracy to
99.5%.
V. C ONCLUSIONSIn this paper we present a robust defense mechanism against
PUE attack in a cognitive radio network. We design GAN
based models considering (a) no prior information and (b)prior information about the PUs. We call them as dumb andsmart GAN models respectively. Through testbed evaluations,
we show that the GAN training for both kind of generators
give a competitive accuracy for EPU or PUE attacker detection[1] M. McHenry, “Spectrum white space measurements,” New America
Foundation Broadband Forum, 2003.
[2] Q. Zhao and B. M. Sadler, “A Survey of Dynamic Spectrum Access,”
IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 79–89, 2007.
[3] FCC ET Docket No. 08-260, “Second Report and Order and Memorandum Opinion and Order – Unlicensed Operation in the TV Broadcast
Bands / Additional Spectrum for Unlicensed Devices Below 900 MHz
and in the 3GHz Band,” US Federal Communications Commission,
Washington DC, FCC 08-260, 2008.
[4] S. Bhattacharjee, S. Sengupta, and M. Chatterjee, “Vulnerabilities incognitive radio networks: A survey,” Elsevier Computer Communications, vol. 36, no. 13, pp. 1387–1398, 2013.
[5] G. Jakimoski and K. P. Subbalakshmi, “Denial-of-Service Attacks on
Dynamic Spectrum Access Networks,” in IEEE International Conference on Communications Workshops, 2008, pp. 524–528.
[6] R. Chen, J. Park, and J. H. Reed, “Defense against Primary User
Emulation Attacks in Cognitive Radio Networks,” IEEE Journal on
Selected Areas in Communications, vol. 26, no. 1, pp. 25–37, 2008.
[7] I. Goodfellow et al., “Generative Adversarial Nets,” in Advances in
Neural Information Processing Systems, 2014, pp. 2672–2680.
[8] S. Chen, K. Zeng, and P. Mohapatra, “Hearing is believing: Detecting
mobile primary user emulation attack in white space,” in Proceedings
IEEE INFOCOM, 2011, pp. 36–40.
[9] R. Chen, J. . Park, and K. Bian, “Robust Distributed Spectrum Sensing
in Cognitive Radio Networks,” in Proceedings IEEE INFOCOM, 2008,
pp. 1876–1884.
[10] Lime Microsystems, “A Pocket Digital TV Transmitter with Raspberry Pi Zero and LimeSDR Mini,” https://www.crowdsupply.com/limemicro/limesdr-mini, 2019.
[11] D. Cabric, S. M. Mishra, and R. W. Brodersen, “Implementation Issues
in Spectrum Sensing for Cognitive Radios,” in Asilomar Conference on
Signals, Systems and Computers, vol. 1, 2004, pp. 772–776.
[12] M. P. Olivieri, G. Barnett, A. Lackpour, and A. D. and, “A Scalable
Dynamic Spectrum Allocation System with Interference Mitigation forTeams of Spectrally Agile Software Defined Radios,” in Proceedings
IEEE DySPAN, 2005, pp. 170–179.
[13] C. Zhao, W. Wang, L. Huang, and Y. Yao, “Anti-PUE Attack Baseon the Transmitter Fingerprint Identification in Cognitive Radio,” inProceedings IEEE WiCom, 2009, pp. 1–5.
[14] Y. Liu, P. Ning, and H. Dai, “Authenticating Primary Users’ Signals in
Cognitive Radio Networks via Integrated Cryptographic and Wireless
Link Signatures,” in Proceedings IEEE SP, 2010, pp. 286–301.
[15] D. Pu, Y. Shi, A. V. Ilyashenko, and A. M. Wyglinski, “Detecting
Primary User Emulation Attack in Cognitive Radio Networks,” inProceedings IEEE GLOBECOM, 2011, pp. 1–5.
[16] Ettus Research, “USRP B210,” https://www.ettus.com/product/details/
UB210-KIT, 2018.
[17] D. Roy, T. Mukherjee, M. Chatterjee, and E. Pasiliao, “Detection ofRogue RF Transmitters using Generative Adversarial Nets,” in Proceedings IEEE WCNC, 2019.
[18] D. Roy, T. Mukherjee, and M. Chatterjee, “Machine Learning in Adversarial RF Environments,” IEEE Communications Magazine, vol. 57,
no. 5, pp. 82–87, 2019.
[19] F. Chollet et al., “Keras: The Python Deep Learning library,”https://keras.io, 2015.
[20] M. Abadi et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” CoRR, 2016.
[21] N. Srivastava et al., “Dropout: A Simple Way to Prevent Neural
Networks from Overfitting,” The Journal of Machine Learning Research,
vol. 15, no. 1, pp. 1929–1958, 2014.
[22] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,”
CoRR, vol. abs/1412.6980, 2014.