DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR COMMUNICATIONS

Machine Learning inAdversarial RF Environments
Debashri Roy, Tathagata Mukherjee, and Mainak Chatterjee

For RFML tasks, deep
feature learners with an
inherent recurrent structure have been shown toperform well. Even so, thefield of RFML is still very
young, and a lot needs tobe done to bridge the gap
between the ML community and the wirelesscommunity for RFML tobe successfully applied forsolving large-scale real-life
problems.

Abstract
With more and more autonomous deployments of wireless networks, accurate knowledgeof the RF environment is becoming indispensable.
Various techniques have been developed over
the years that can not only assess the RF environment but can also characterize the various radiotransmitters (sources) that define the ambient RF
environment. Machine learning techniques haveshown promise for such characterizations throughthe development of RF machine learning (RFML)
systems delivering autonomous control. Althoughclassical machine learning techniques work well
for a large variety of tasks, they have not done
as well for RFML. For RFML tasks, deep featurelearners with an inherent recurrent structure havebeen shown to perform well. Even so, the fieldof RFML is still very young, and a lot needs to bedone to bridge the gap between the ML community and the wireless community for RFML to be
successfully applied for solving large-scale real-life
problems. This article is a step in that direction.

Introduction

The ubiquitous use of wireless services and applications has become ingrained in every aspect of
our lives. We depend on wireless technologies
not only for our smartphones but also for otherapplications like telemetry, surveillance, emitter
location, radio navigation, jamming, anti-jamming,radar detection, unmanned aerial vehicle (UAV)surveillance, navigation, and location tracking.
Applications like teleconferencing and telemedicine, which traditionally depended on wired
networks, have migrated to the wireless domain.
With such large-scale dependence on use of the
RF spectrum, it becomes imperative that we manage and use the limited available spectrum in themost efficient manner possible. In order to dothat, one needs to better understand the ambient
signal characteristics for optimal deployment of
wireless infrastructure and efficient resource provisioning.
Design of new wireless technologies anddeployment of wireless networks using thosetechnologies must take into consideration several factors including detection and monitoring of
encroachment, ability to predict RF propagationand coverage, techniques to mitigate noise, policies enabling spectrum sharing, characterizationof frequencies and waveforms, coverage analysis
for optimal deployment, detection and de-con-

fliction, and more importantly, identification ofadversarial RF signals. Furthermore, the adventof dynamic spectrum access enabled by the useof software defined radios is pushing the frontiers of wireless communications. These radios are
expected to constantly monitor the radio environment and the resulting data can be used to learn
about their surroundings so that they can intelligently adapt their RF parameters (e.g., operating
frequency, bandwidth, waveform, modulation,
noise mitigation) to meet their desired objectives.
In order to best use the radio resources in boththe spatial and time domains, and to maximize
the spectral efficiency, past and current knowledge of the RF signals are important. Althoughsensing mechanisms can be used to assess thecurrent environment, learning techniques are
typically used to analyze the past observations
and predict future occurrences of events related
to a signal. With the proven success of machinelearning (ML) techniques in various domains, suchtechniques are also being sought for characterizing and understanding the RF environment. Someof the goals of the learning techniques in the RF
domain are emitter fingerprinting, emitter localization, modulation recognition, feature learning,
attention and saliency, autonomous RF sensor
configuration, and waveform synthesis.
ML techniques allow radios to learn and adapt
their RF parameters so as to optimize their respective objectives. Such adaptation by the radios isachieved by exposing their configuration options
to make the operational parameters flexible andtunable. As a consequence, the configurability and adaptability features open up avenues
for manipulation as well where a radio can be
induced to learn false information by adversaries[1]. This creates a unique set of challenges in thedomain of RFML systems, which makes implementing ML algorithms for RF systems far morechallenging.
In this article, we discuss the recent trend offacilitating learning in the RF domain using different ML approaches. These ML techniques havetheir own strengths and weaknesses dependingon the context and the type of dataset being
used. We start by broadly classifying the ML techniques into supervised and unsupervised learning,and highlight the various schemes that have beenused in the RF domain. Then we discuss five techniques that are currently being widely exploredas they exhibit promising results for future implementations. These are:

Digital Object Identifier:10.1109/MCOM.2019.1900031

Debashri Roy and Mainak Chatterjee are with the University of Central Florida; Tathagata Mukherjee is with the University of Alabama.

820163-6804/19/$25.00 © 2019 IEEE

IEEE Communications Magazine • May 2019

Machine Learning
Algorithms in RF Domain

Unsupervised

Supervised

K Nearest
Neighbours

Decision
Trees

BayesianClassifier

Naive
Bayes

SVM

DNN

NeuralNetworks

CNN

K Means
Clustering

RNN

Hierarchical
Clustering

BaysianLearning

Nonparametric

PCA

NeuralNetworks

Generative
Adversarial Nets

Figure 1. Classification of machine learning algorithms in the RF domain.
• Support vector machine (SVM)• Deep neural network (DNN)• Convolutional neural network (CNN)• Recurrent neural network (RNN)• Non-parametric Bayesian classifier
SVM and the three neural networks (NNs)perform better with continuous and multi-dimensional datasets, which could be leveraged when
the RF signals contain multiple attributes. However, applicability of SVM and NN comes at a cost:they exhibit high variance sensitivity. On the other
hand, Bayesian classifiers are advanced statisticaltechniques for classifying and identifying features.
We discuss how a non-parametric version of theBayesian classifier with an infinite Gaussian mixture
model can be used for emitter identification without prior knowledge of the number of transmitters(or classes) or their characteristics. In spite of thesedevelopments, we argue that the ML techniques
have their limitations in an adversarial setting (i.e., ifadversarial RF signals are present during the learning and/or classification process). We show thatapproaches based on the relatively new generative
adversarial nets (GANs) are well suited for learning in adversarial RF environments, and are able todistinguish between adversarial and trusted signals
and sources. We implement the discriminative andgenerative models of a GAN on software definedradios (SDRs) and show that the model could classify four transmitters as well as detect rogue transmitters with very high accuracy.

mAchIne LeArnIng For rF sIgnALs

The basic work flow of any ML algorithm starts
with digesting the feature space. Most often, thequality of the model learned by the algorithm
depends heavily on the quality of the features
used as input to the algorithm, which in turndepends on the problem. Thus, for example, eventhough the RF data may consist of the received
signal strength indicator (RSSI) values for a given
problem, using the raw input in this form may not
result in the optimal model. In such cases it might
be helpful to transform the raw input features intoa higher/lower dimensional feature space thatsuccinctly captures the essence of the problem,
thereby making it easier to learn better ML models for the problem.
Figure 1 shows the classification of different
ML algorithms that have been used for learningin the RF domain. All learning techniques broadlyIEEE Communications Magazine • May 2019

fall under either supervised or unsupervised training mechanisms.

supervIsed LeArnIng In the rF domAIn

Supervised learning algorithms are a set of learning algorithms where a set of mutually exclusive
labeled data is used for building the learningmodel (also called training). One of the simplest
supervised learning algorithms is based on the
K-nearest neighbor (KNN) search. The KNN algorithm classifies previously unseen data based on
the labels of the nearest data samples that areincluded in the training set. Algorithms based on
KNN searches are usually very computation-intensive, especially in the higher dimensions where thedimension of the feature space factors into therunning time of the known algorithms for computing KNNs. This also makes algorithms basedon KNN search unsuitable for the RF domain. Furthermore, KNN may not provide optimal performance for a higher-dimensional dataset, which isoften the case for RF signal data. Another set ofsupervised learning algorithms are based on theidea of decision trees. Decision trees are used
to assign specific class labels to the items using
predictive modeling based on the values of thefeatures associated with the item of interest. The
decision trees perform well with high-dimensional data, which could be advantageous for handling multi-dimensional RF data; however, longer
training time and lack of accuracy could hinder
usefulness in mission-critical applications, as ismost often the case with RF processing. Another important class of supervised algorithms are
based on the idea of Bayesian classifiers. Bayesian classifiers predict the probability of a given
sample belonging to a particular class using a priori knowledge. A specific category of Bayesianclassifier is the Naive Bayes, where one assumes
that each feature contributes toward the classification and are mutually correlated. Since different parameters from the same kind of radios
can be considered as mutually correlated, Naive
Bayes can potentially be used successfully in the
RF domain. Naive Bayes also works reasonably
well with limited training data and is less likely tosuffer from overfitting. However, the predictions
are less accurate compared to the other methods
like neural networks [3, 4]. We defer our discussions on SVMs, DNN, CNN, and RNN to a later
section as they have shown promising results in83

Unsupervised learningalgorithms are a set ofalgorithms where there
is no explicit training
phase for building amodel from labeleddata as is done with thesupervised algorithms.
These algorithms makeinference from unlabeled data, most often
exploiting the observed
variance of the data andtheir association relative
to each other.

the RF domain and thus deserve to be discussed
in detail.

Unsupervised Learning in the RF Domain

Unsupervised learning algorithms are a set ofalgorithms where there is no explicit training
phase for building a model from labeled data as
is done with the supervised algorithms. Thesealgorithms make inference from unlabeled data,
most often exploiting the observed variance ofthe data and their association relative to eachother. K-means clustering is one such learningalgorithm where the observed data is partitioned
into clusters using information about the distance
between the data points (or more generally using
the similarity between the observed data points).
New data is assigned to a cluster such that thedata point is closest to a particular cluster mean.
Another set of clustering algorithms are based on
the idea of hierarchies. Here the items of interest
are progressively partitioned into a hierarchy ofclusters; the clusters that are higher up in the hierarchy are coarse, whereas the ones that are lower
are more fine-grained. Hierarchical clustering canbe used for transmitter identification and classification where the number of entities are unknown.
Another set of unsupervised algorithms concerns
the problem of dimensionality reduction. Dimensionality reduction aims to identify a subspace ofthe feature space such that the projection of thedata into the subspace (which has a lower dimension than the feature space) would explain mostof the variation observed in the data.
Principal component analysis (PCA) is one
such method for dimensionality reduction anddata compression. PCA can be very useful forthe multi-dimensional RF data as it can be leveraged to speed up the underlying classification or
regression algorithms when faster online learningand processing is required.
The final and most important category of unsupervised algorithms for the RF domain is the generative adversarial networks (GANs) [2]. It is arelatively new class of algorithms that have beenshown to perform well in adversarial settings.
GANs use a generative model that enables therealistic generation of samples from a given distribution, which can then be used to train a discriminator for identifying real samples drawn fromthe distribution as opposed to fake ones obtained
from the generator. We dedicate a section todescribing GANs as they have been shown to perform very well in adversarial settings. Likewise,
non-parametric Bayesian learning is discussed
later due to its relevance in the RF domain.

Important ML Algorithms for
RF Domain

In this section, we describe the ML techniquesthat have been most successful in characterizing
the RF environment by identifying and differentiating signals from different kinds of transmitters
including broadcast radio, local and wide area
data and voice radios, radars, and so on.

Support Vector Machine

SVM has been one of the most successful classical ML algorithms and has been applied to avast array of problems. SVM is a discriminative

84

classifier using supervised learning and generates an optimal hyperplane for data classificationand identification. At its heart, SVM is a binaryclassifier which assumes that the data is linearly
separable and computes the optimal separating
hyperplane by solving a quadratic program on thespace defined by the training data.
SVM has been applied to the RF domain fortransmitter identification using fingerprinting. In[5], Kroon et al. presented an RF fingerprinting
technique for banning prohibited transmittersfrom accessing the cellular base stations. They
proposed a customized ensemble classifier basedon the probability density of an SVM classifier,which achieved 97 percent true positive and 80
percent true negative rates.
However, in recent times, the research trends
on RF fingerprinting are shifting toward using raw
signal data as compared to using hand engineeredfeatures. This has also been facilitated by the availability of automatic feature learning systems likethe multi layer perceptron. In [4], Youssef et al.
investigated different ML strategies including SVM
and neural networks using raw I/Q data rather
than using any hand-engineered features. I/Qdata consists of a complex-valued in-phase (I) and
quadrature (Q) component in a signal data constellation. Their implementation produces goodtraining accuracy of 87.6 percent but poor test
accuracy of 67.6 percent.
SVM classifiers are relatively easy to implement and can be extended for higher-dimensional
data. However, achieving high accuracy remainsa challenge. Furthermore, SVM implementations
do not typically consider any adversarial situation; thus, when a malicious entity tries to pass asa trusted device, the SVM classifier has no wayof determining its true identity and thus would
incorrectly classify it as one of the trusted devices. This makes the possibility of using SVMs inadversarial settings quite low. Moreover, SVMs
require hand-engineered features even when raw
I/Q data is used. As a result of these drawbacks
and the recent resurgence of NNs, attention has
turned to using the same for RFML applications.

Deep Neural Networks

Deep neural networks have revolutionized thefield of artificial intelligence in the last few years.
The problems tackled by DNNs range over computer vision, natural language processing, speechprocessing, and so on. They have been shown toperform better than humans for some of these
problems. More recently, [3, 6] have shown theefficacy of using DNNs in RF communication systems. In [3], Shea et al. presented a modulation
recognition approach using a DNN, achieving
nearly 82–87 percent accuracy. They used a synthetic dataset consisting of 11 widely used modulations: 8 digital and 3 analog modulations. Their
RF fingerprinting was based on the modulation
type, which was used as the primary input feature
for the network, which then computes more complex features for model learning.
The automatic features learned by DNNs
are often better and more informative than thehand-engineered features used for SVMs, andhence the DNNs yield better accuracy. However,
DNNs do not perform as well for datasets withspatial and temporal correlations, which is theIEEE Communications Magazine • May 2019

case for the RF domain. RF signals exhibit highspatial correlation (e.g., modulation schemes),
high temporal correlation (e.g., I/Q signal data),
or both, and in order for a system to work wellwith RF data, these correlations need to be
exploited by the learning system. Furthermore,
DNNs have proven to be susceptible to malicious
attacks and fail to distinguish rogue transmittersfrom trusted ones [7] in the presence of active
adversaries.

Convolutional Neural Networks

Fully connected DNNs are like standard NNs butwith a lot more hidden layers. However, these
networks can be augmented with convolutional layers for faster training and for enabling the
network to learn more compact and meaningful
representations. Deep convolutional neural networks (DCNNs) have been shown to be effective for several different tasks in communication.
There have been quite a few attempts at using
DCNNs for learning different RF parameters. One
such effort is presented in [7], where Shea et al.
presented an optimized DCNN model (with 18
layers) for recognition of modulation schemes fora large synthetic dataset (consisting of 24 modulation schemes), as well as over-the-air data. 94
percent accuracy on synthetic data and 87 percent accuracy on over-the-air data were achieved.
Inspired by this and buoyed by the success of theapplication of CNNs in communication, Youssef
et al. presented a CNN architecture [4] for deep
feature embedding, transmitter identification, andclassification.
A CNN works with several filters that capture
the spatial correlation within the input features
for the purpose of computing lower-level featuresthat effectively capture the spatial correlation
between the input features. A cascade of such
filters are used for propagation of these features
through multiple layers, in effect computing more
low-level features, thereby giving the best solution
for datasets having spatially correlated features.
However, it does not perform well in general fordatasets where the features are uncorrelated, forexample, the rise time, which is a feature that canbe computed for many RF datasets. CNNs may
not perform well if the nature of the correlations
change from the training data to the test data.
This is because in such cases the features computed through the different filters on the training data
will not be applicable to the test data. It is also notrecommended for time series data where temporal correlations might exist. Moreover, CNNs havethe same limitations as DNNs in regard to immunity from malicious attacks.

Recurrent Neural Networks

Recurrent neural networks are capable of predictions with time series data. They have beenshown to work well with speaker recognition
tasks [8]; inspired by this and based on the factthat the raw RF signal data from the transmitter
represent a time series, the RNN can be considered as a potential model to build a system forlearning transmitter embedding and classification.
One variant of the RNN is long short-term memory (LSTM) [9], which has been successfully used
for modeling temporal data such as speech. The
problem of estimating the noise from the signal

IEEE Communications Magazine • May 2019requires analysis of the temporal data because
the noise characteristics can only be estimated bylooking at the received signal over time.
In order to estimate the noise, any systemneeds to “listen” to the underlying signal for sometime and “remember” the same for noise estimation. Previously, NNs lacked this capability. Another issue was the problem of vanishing gradients
when trying to use back propagation with temporal data. However, both these problems weresolved by the invention of gated units, such as
the LSTM, gated recurrent unit (GRU), and theirvariants.
In [10], Sreeraj et al. presented a 2-layer LSTM
model to perform modulation recognition overthe synthetic dataset of 11 modulation schemes.
Accuracies close to 90 percent were achieved fordata with high signal-to-noise ratio coupled withtime domain data. This implementation establishes the feasibility of using RNN models for learning
RF features related to time domain analysis. However, the effectiveness of LSTM models for learning other RF parameters is still an open researchchallenge.
The RNN performs well with temporally correlated datasets through the implementation of
the concepts of “memory” and “gates.” However, it responds poorly to spatially correlated data.
RNN implementations also incorrectly classify
rogue data as trusted when active adversaries
spoof the signals as coming from trusted sourcesin cases where they are not.

Recurrent neural networks are capable ofpredictions with timeseries data. They havebeen shown to work
well with speaker recognition tasks; inspiredby this and based on
the fact that the raw
RF signal data from thetransmitter represent atime series, the RNN
can be considered as apotential model to builda system for learningtransmitter embedding
and classification.

Non-Parametric Bayesian Classifier

A non-parametric Bayes classifier is an unsupervised learning strategy that uses a probability
density estimator to determine the probabilityof an observation belonging to a particular class.
Nguyen et al. presented a non-parametric Bayesian learning [11] approach to identify wirelessdevices by characterizing their fingerprints. They
considered a device-dependent feature spacemodeled as multivariate Gaussian distribution, which includes frequency difference andphase shift difference as dominant features. The
non-parametric Bayesian learning approach isthen employed over the generated distributions
and used to identify the unknown number ofclusters. Experimental results reveal that the proposed method was capable of clustering 1 to 4
Zigbee transmitting devices with a 100 percent
hit rate. Next, they showed that the proposed RF
fingerprinting approach can be applied for intrusion detection for Sybil and masquerade attacks
by spoofing the medium access control (MAC)
address. There is still a dearth of methods thatare resilient to any general type of attacks for
RFML systems.

Comparison of Different ML Techniques

Analyzing the potential of different kinds of ML
approaches in RF parameter learning, we summarize that:• SVM struggles to achieve high accuracies forhigher dimensional datasets• The DNN is best suited for fixed valued RFparameters (e.g., rise time).
• The CNN works well with RF parameters thatexhibit high spatial correlations (e.g., modulation techniques).

85A premise for ML techniques to work is thatthe data used to train
the systems is accurate
and faithfully represents
the distribution that
describes the underlying
data generation process. Hence, the model
learned from this data isable to generalize. However, in an adversarial
setting, we cannot anticipate the adversary’s
moves; thus, there is no“clean” training data to
begin with.

• The RNN is the best option for RF parameters with high temporal correlations (e.g., I/Q
signal data).
• Non-parametric Bayesian classifiers are limited
to specific types of applications and datasets.
It must be noted that all the aforementioned
ML techniques are susceptible to attackers. Oncethe attacker gets to know the features used by thelearning engine, it becomes easy for the attacker tomislead the learning process. The same applies in
the RF domain where an RF transmitter can spoof
the signals of others and remain undetected.
Next, we discuss the importance of adversarial learning and how it overcomes the shortcommings of the other ML approaches. We show how
GANs are not only robust to the ever-changing
wireless environment but are also able to operate
in the presence of active adversaries.

Adversarial Learning via GAN

Most of the ML techniques mentioned above
can handle static (data) attacks after the model
has been trained. Thus, once the trained model
is available, the algorithm will not be affected bymalicious changes to the test data. This is true for
RF data as well. Note, however, that active attacks,
where the adversary can modify the training data,
are more problematic. In fact, detection of adversarial effects on RF data that exhibit dynamic statistical properties is extremely challenging andnontrivial. A premise for ML techniques to workis that the data used to train the systems is accurate and faithfully represents the distribution that
describes the underlying data generation process.
Hence, the model learned from this data is able
to generalize. However, in an adversarial setting,
we cannot anticipate the adversary’s moves; thus,
there is no “clean” training data to begin with.
Moreover, there are ways to pollute the training
examples intentionally. Thus, most ML techniques
would fail in such settings, as was made evident in[12]. To make matters worse, sophisticated adversaries would exploit the online learning mechanisms used by the ML systems and therefore beable to adopt strategies to mislead the learning
process. This motivates the design of adversarialmachine learning algorithms that can combat thepresence of adversaries during and/or after the
training phase. This makes it a difficult problem
from both the perspective of ML as well as that ofcommunications. Thus, it merits consideration andrecently has become an open research area.

Generative Adversarial Network

As mentioned before, one of the main problemsof using DNNs for learning in adversarial settings
is the possibility that the learned model is inaccurate because of interference of the adversary. In
order to circumvent this problem, one possibility
is to use DNNs in conjunction with GANs.
Commonly used ML techniques use a discriminative model, which learns a function that maps
the input data to some desired output class label(i.e., they learn the conditional distribution). On
the contrary, a generative model tries to learn
the joint probability of the input data and labels
simultaneously, which can be converted to conditional probability for classification via Bayes rule.
Generative adversarial networks, introduced by
Goodfellow et al. in 2014 [2], are a class of arti-

86

ficial intelligence algorithms used in unsupervised
ML. In a GAN, a system of two NNs contend witheach other. One is called the generator G, andthe other is called the discriminator D. The generator’s job is to create fake data that would have
similar attributes to those in the actual training
dataset. The discriminator’s job is to identify thegenerated data as real or fake. As this processcontinues, both G and D get better at generating fake data and identifying them, respectively.
Both models learn from each other and are driven
toward an equilibrium under certain assumptions.
In the context of RF signals, the adversary is
like the generator G, which tries to generate fakeRF data and evade detection at the same time.
The defender is the discriminator D, which tries
to distinguish the fake signal from the real ones.
Both the adversary and the defender act in around-robin manner, where the adversary learns
to come up with new strategies to overpower the
defense mechanisms employed by the defender. Both learn and adopt better strategies overtime. The objective is to train D to maximize theprobability of assigning the correct label (i.e., thedetection problem) and train G simultaneously.
Since G’s costs depend on D’s parameters andvice versa, none can control the other’s parameters. This situation is best represented as a game,
more specifically a min-max game. The objectiveof this min-max game is to find the Nash equilibrium that minimizes the maximum loss. GANs aredifferent from most of the current mechanismsthat emphasize strengthening the defense mechanism and ignore how the adversary might also
strengthen its strategies over time.

GAN Implementation

In order to prove the efficacy of GANs in an
adversarial setting, we implement the discriminator D and the generator G for the problem oftransmitter identification through the use of RF
fingerprints.
Data Collection and Design: We design thediscriminator and generator separately, as shownin Fig. 2 As for the training phase, we collectover-the-air radio signal data from four software
defined radios (T1-T4). We used Universal Software Radio Peripheral (USRP) B210s [13]. The
adversarial network helps to strengthen both thegenerative and discriminative models throughmultiple iterations.
Generator: The adversary helps the discriminator to be robust enough to fight against future
real adversaries. We model the generator to learn
the distribution of different features from the sample space. The generator is also fed with additive
white Gaussian noise along with the features togenerate fake signal data. Over time, the generator learns the probability distribution over the
sample space of the input.
Discriminator: The discriminator is trained
over both real and fake data created by the generator. The model learns by minimizing a costfunction during training, which depends on both
the generator and the discriminator. In each
iteration the discriminator is trained to become
smarter to identify the signals generated by thegenerator. Although in theory the game between
the generator and the discriminator can go on
indefinitely, depending on the ratio of data andIEEE Communications Magazine • May 2019concLusIons

In order to optimally use the radio resources, it isimperative to develop techniques that can learn,characterize, and predict the wireless RF environment efficiently. In this article, we discuss some ofthe supervised and unsupervised learning strategies that have been used to investigate different
types of problems in the RF domain. However,most of these methods suffer from an adversarial
disadvantage. We demonstrate the efficacy of a
GAN-based approach for identification of transmitters in adversarial RF environments.

reFerences[1] S. Bhattacharjee, S. Sengupta, and M. Chatterjee, “Review:
Vulnerabilities in Cognitive Radio Networks: A Survey,”
ELSEVIER COMCOM, vol. 36, no. 13, 2013, pp. 1387–98.
[2] I. Goodfellow et al., “Generative Adversarial Networks.”
NIPS, 2014, pp. 2672–80.
[3] T. O’Shea, J. Corgan, and T. Clancy, “Convolutional Radio Modulation Recognition Networks,,” EANN, 2016, pp. 213–26.
[4] K. Youssef et al., “Machine Learning Approach to RF Transmitter Identification,” CoRR, vol. arXiv:1711.01559, 2017.
[5] B. Kroon et al., “Steady State RF Fingerprinting for Identity
Verification: One Class Classifier Versus Customized Ensemble,” AICS, 2010, pp. 198–268.
[6] T. O’Shea and J. Hoydis, “An Introduction to Deep Learning
for the Physical Layer,” IEEE TCCN, vol. 3, no. 4, 2017, pp.
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[7] T. O’Shea, T. Roy, and T. Clancy, “Over-the-Air Deep Learning Based Radio Signal Classification,” IEEE JSTSP, vol. 12,
no. 1, 2018, pp. 168–79.
[8] J. Ren et al., “Look, Listen and Learn-A Multimodal LSTM for
Speaker Identification,” AAAI, 2016, pp. 3581–87.

IEEE Communications Magazine • May 2019

FeatureSpace

FeatureExtractor

Generator
G

Discriminator
D

Noise

Is
Real
TuningParameters

Raw Signal
Data

Real
Data

model density, the discriminator overpowers thegenerator most of the time. This happens due tothe vanishing gradient for the generator. In practice, this phenomenon results in generating moreaccurate ML classifiers. The discriminator is typically implemented as a deeper network than thegenerator to get a purposeful implementation of
the GAN framework.
Configuration: We train both the generator
and discriminator through iterative sequential
learning to strengthen the generative model overtime. We model the generator as a three-layer
NN with sigmoid activation, generating Fakeradio signal data. The discriminator is modeled asa five-layer deep network with a softmax activation at the end. We use categorical cross-entropy
training on an Adam optimizer for gradient-based
optimization. We implement the GAN in Python
using ML libraries Keras as the frontend and Tensorﬂow as the backend.
Results: We notice that the discriminator isable to detect the fake transmitters (generated by
the generator) with 50 percent accuracy before
the GAN training. After several epochs (< 50) oftraining, the optimal discriminator (D*) is able todetect the Fake transmitters with about 90 percent accuracy, as shown in the confusion matrix
in Fig. 3. The proposed model is able to recognize the rogue transmitters as well as categorize
the trusted ones (T1–T4) with similar accuracy. It
is evident that the number of false positives andfalse negatives in the confusion matrix are low
and looks promising for future advancement of
GAN oriented research in the RF domain.

Figure 2. A simplified view of GAN implementation.

Figure 3. Confusion matrix for determining four
trusted and one fake transmitters.
[9] F. Gers, J. Schmidhuber, and F. Cummins, “Learning to Forget: Continual Prediction with LSTM,” IDSIA, tech. rep.,
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Sensors,” IEEE TCCN, vol. 4, no. 3, 2018, pp. 433–45.
[11] N. T. Nguyen et al., “Device Fingerprinting to Enhance
Wireless Security Using Nonparametric Bayesian Method,”IEEE INFOCOM, 2011, pp. 1404–12.
[12] N. Papernot et al., “Practical Black-Box Attacks Against
Machine Learning,” ACM ASIACCS, 2017, pp. 506–19.
[13] D. Roy et al., “Detection of Rogue RF Transmitters Using
Generative Adversarial Nets,” IEEE WCNC, 2019.

bIogrAphIes

DEBASHRI ROY (debashri@cs.ucf.edu) received her M.S. degree
in computer science from the University of Central Florida,
where she is currently a Ph.D. candidate. Her research interests
are in the areas of machine learning, wireless communication,and video QoE.
TATHAGATA MUKHERJEE (tm0130@uah.edu) is an assistant professor of computer science at the University of Alabama in
Huntsville. He obtained his M.S. and Ph.D. in computer sciencefrom Florida State University. His interests are in cyber security,
adversarial machine learning, cognitive radio networks, optimization, and graph theory. Prior to his current position ,he wasthe chief scientist at Intelligent Robotics Inc., a non-profit DoD
research lab.
MAINAK CHATTERJEE (mainak@cs.ucf.edu) is an associate professor in the Department of Computer Science at the University ofCentral Florida, Orlando. He received his B.Sc. degree in physics(Hons.) from the University of Calcutta, his M.E. degree in electrical communication engineering from the Indian Institute of
Science, Bangalore, and his Ph.D. degree in computer sciencefrom the University of Texas at Arlington. His research interests
include economic issues in wireless networks, applied gametheory, cognitive radio networks, dynamic spectrum access, andmobile video delivery.

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