@article{medaiyese_ezuma_lauf_guvenc_2022, title={<p>Wavelet transform analytics for RF-based UAV detection and identification system using machine learning & nbsp;</p>}, volume={82}, ISSN={["1873-1589"]}, url={https://doi.org/10.1016/j.pmcj.2022.101569}, DOI={10.1016/j.pmcj.2022.101569}, abstractNote={In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm called SqueezeNet, as classifiers. In RF signal fingerprinting research, the transient and steady state of the signals can be used to extract a unique signature from an RF signal. By exploiting the RF control signals from unmanned aerial vehicles (UAVs) for DDI, we considered each state of the signals separately for feature extraction and compared the pros and cons for drone detection and identification. Using various categories of wavelet transforms (discrete wavelet transform, continuous wavelet transform, and wavelet scattering transform) for extracting features from the signals, we built different models using these features. We studied the performance of these models under different signal-to-noise ratio (SNR) levels. By using the wavelet scattering transform to extract signatures (scattergrams) from the steady state of the RF signals at 30 dB SNR, and using these scattergrams to train SqueezeNet, we achieved an accuracy of 98.9% at 10 dB SNR.}, journal={PERVASIVE AND MOBILE COMPUTING}, publisher={Elsevier BV}, author={Medaiyese, Olusiji . O. and Ezuma, Martins and Lauf, Adrian P. and Guvenc, Ismail}, year={2022}, month={Jun} }
 @article{ezuma_anjinappa_semkin_guvenc_2022, title={Comparative Analysis of Radar-Cross-Section-Based UAV Recognition Techniques}, volume={22}, ISSN={["1558-1748"]}, url={https://doi.org/10.1109/JSEN.2022.3194527}, DOI={10.1109/JSEN.2022.3194527}, abstractNote={This work investigates the problem of unmanned aerial vehicle (UAV) recognition using their radar cross section (RCS) signature. The RCS of six commercial UAVs is measured at 15 and 25 GHz in an anechoic chamber for both vertical–vertical (VV) polarization and horizontal–horizontal (HH) polarization. The RCS signatures are used to train 15 different recognition algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that, while the average accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3-dB SNR using the 15-GHz VV-polarized RCS test data from the UAVs. We investigate the recognition accuracy using the Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by Peter Swerling’s statistical models and the discriminant analysis ML model. In general, the accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that, while the SL algorithms achieved good recognition accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average recognition time of about 0.46 ms.}, number={18}, journal={IEEE SENSORS JOURNAL}, author={Ezuma, Martins and Anjinappa, Chethan Kumar and Semkin, Vasilii and Guvenc, Ismail}, year={2022}, month={Sep}, pages={17932–17949} }
 @article{medaiyese_ezuma_lauf_adeniran_2022, title={Hierarchical Learning Framework for UAV Detection and Identification}, volume={6}, ISSN={["2469-7281"]}, DOI={10.1109/JRFID.2022.3157653}, abstractNote={The ubiquity of unmanned aerial vehicles (UAVs) or drones is posing both security and safety risks to the public as UAVs are now used for cybercrimes. To mitigate these risks, it is important to have a system that can detect or identify the presence of an intruding UAV in a restricted environment. In this work, we propose a radio frequency (RF) based UAV detection and identification system by exploiting signals emanating from both the UAV and its flight controller, respectively. While several RF devices (i.e., Bluetooth and WiFi) operate in the same frequency band as UAVs, the proposed framework utilizes a semi-supervised learning approach for the detection of UAV or UAV’s control signals in the presence of other wireless signals such as Bluetooth and WiFi. The semi-supervised learning approach uses stacked denoising autoencoder and local outlier factor algorithms. After the detection of UAV or UAV’s control signals, the signal is decomposed by using Hilbert-Huang transform and wavelet packet transform to extract features from the time-frequency-energy domain of the signal. The extracted feature sets are used to train a three-level hierarchical classifier for identifying the type of signals (i.e., UAV or UAV control signal), UAV models, and flight mode of UAV.}, journal={IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION}, author={Medaiyese, Olusiji O. and Ezuma, Martins and Lauf, Adrian P. and Adeniran, Ayodeji A.}, year={2022}, pages={176–188} }
 @article{ezuma_anjinappa_funderburk_guvenc_2022, title={Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies}, volume={58}, ISSN={["1557-9603"]}, url={https://doi.org/10.1109/TAES.2021.3096875}, DOI={10.1109/TAES.2021.3096875}, abstractNote={This article presents a radar cross-section (RCS)-based statistical recognition system for identifying/classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the article presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.60% or better is achievable.}, number={1}, journal={IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS}, author={Ezuma, Martins and Anjinappa, Chethan Kumar and Funderburk, Mark and Guvenc, Ismail}, year={2022}, month={Feb}, pages={27–46} }
 @article{nwachioma_ezuma_medaiyese_2021, title={FPGA prototyping of synchronized chaotic map for UAV secure communication}, ISSN={["1095-323X"]}, DOI={10.1109/AERO50100.2021.9438428}, abstractNote={We propose a security architecture that uses the principle of chaos for UAV secure communication. A UAV, identified as an aerial base station (ABS), communicates with a ground base station (GBS) over a wireless radio frequency (RF) channel. The communication units of the ABS and GBS have dynamics according to the logistic map. The map is chaotic in the appropriate parameter space. Its states are non-periodic, broadband, and noise-like in the frequency domain. They are useful for spreading information data during transmission, making it extremely difficult for an eavesdropper to recover the modulated message since state prediction is ultimately impossible. To retrieve it, we propose a variable feedback controller. We prove that it can asymptotically stabilize the error dynamics when the information source is off. During transmission, the controller synchronizes the units such that the error contains signatures of the information signal. Therefore, the information signal is retrievable by a suitable detection mechanism. Security depends on the confidentiality of the map, the variable feedback controller, including its scale factor and bounded feedback gain and the designer's choice of invertible function for use in the scrambling and descrambling process. Also, the method is less prone to jamming attacks and multipath effects as the broadband spectrum can be used to randomly select RF channels. It uses only a few simple algorithms, including a correlation summation and a detection mechanism. The algorithms collect subsamples of the received signal sequences and averages over each subsample length. The method requires minimal programming efforts and low hardware resource utilization. It is energy-efficient, which is a vital consideration for any UAV security model. Moreover, we realize a prototype of the communication system on field-programmable gate arrays (FPGAs). We presented a digital design of the secure communication system involving the transmission of bitstreams between the ABS and GBS.}, journal={2021 IEEE AEROSPACE CONFERENCE (AEROCONF 2021)}, author={Nwachioma, Christian and Ezuma, Martins and Medaiyese, Olusiji O.}, year={2021} }
 @article{medaiyese_ezuma_lauf_guvenc_2021, title={Semi-supervised Learning Framework for UAV Detection}, DOI={10.1109/PIMRC50174.2021.9569452}, abstractNote={The use of supervised learning with various sensing techniques such as audio, visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in the detection of unmanned aerial vehicles (UAV) in an environment. However, little or no attention has been given to the application of unsupervised or semi-supervised algorithms for UAV detection. In this paper, we propose a semi-supervised technique and architecture for detecting UAVs in an environment by exploiting the RF signals (i.e., fingerprints) between a UAV and its flight-controller communication under wireless inference such as Bluetooth and WiFi. By decomposing the RF signals using a two-level wavelet packet transform, we estimated the second moment statistic (i.e., variance) of the coefficients in each packet as a feature set. We developed a local outlier factor model as the UAV detection algorithm using the coefficient variances of the wavelet packets from WiFi and Bluetooth signals. When detecting the presence of RF-based UAV, we achieved an accuracy of 96.7% and 86% at a signal-to-noise ratio of 30 dB and 18 dB, respectively. The application of this approach is not limited to UAV detection as it can be extended to the detection of rogue RF devices in an environment.}, journal={2021 IEEE 32ND ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC)}, author={Medaiyese, Olusiji O. and Ezuma, Martins and Lauf, Adrian P. and Guvenc, Ismail}, year={2021} }
 @article{ezuma_erden_anjinappa_ozdemir_guvenc_2020, title={Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference}, volume={1}, ISSN={["2644-125X"]}, url={https://doi.org/10.1109/OJCOMS.2019.2955889}, DOI={10.1109/OJCOMS.2019.2955889}, abstractNote={This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system. The system uses a multistage detector to distinguish signals transmitted by a UAV controller from the background noise and interference signals. First, RF signals from any source are detected using a Markov models-based naïve Bayes decision mechanism. When the receiver operates at a signal-to-noise ratio (SNR) of 10 dB, and the threshold, which defines the states of the models, is set at a level 3.5 times the standard deviation of the preprocessed noise data, a detection accuracy of 99.8% with a false alarm rate of 2.8% is achieved. Second, signals from Wi-Fi and Bluetooth emitters, if present, are detected based on the bandwidth and modulation features of the detected RF signal. Once the input signal is identified as a UAV controller signal, it is classified using machine learning (ML) techniques. Fifteen statistical features extracted from the energy transients of the UAV controller signals are fed to neighborhood component analysis (NCA), and the three most significant features are selected. The performance of the NCA and five different ML classifiers are studied for 15 different types of UAV controllers. A classification accuracy of 98.13% is achieved by k-nearest neighbor classifier at 25 dB SNR. Classification performance is also investigated at different SNR levels and for a set of 17 UAV controllers which includes two pairs from the same UAV controller models.}, journal={IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Ezuma, Martins and Erden, Fatih and Anjinappa, Chethan Kumar and Ozdemir, Ozgur and Guvenc, Ismail}, year={2020}, pages={60–76} }
 @article{nwachioma_humberto perez-cruz_jimenez_ezuma_rivera-blas_2019, title={A New Chaotic Oscillator-Properties, Analog Implementation, and Secure Communication Application}, volume={7}, ISSN={["2169-3536"]}, DOI={10.1109/ACCESS.2018.2889964}, abstractNote={This paper reports a new 3-dimensional autonomous chaotic system with four nonlinearities. The system is studied with respect to its numerical solutions in phase space, including sensitive dependence on initial conditions, equilibrium points, bifurcation, and maximal Lyapunov exponent. It is shown that the system is dissipative and has a fractional Lyapunov dimension. Besides, a basin of attraction is determined by the Newton–Raphson’s method. To show its practicality, the new system is implemented by means of an analog electronic circuit. Aperiodicity of the experimental signal is verified by means of an improved power spectral density estimator, viz., the Welch’s method. Also, the correlation dimension is estimated from the experimental time series with the result confirming that the responses are deterministic chaos. Finally, an electronic design of a secure communication application is carried out, wherein a nontrivial square wave is modulated by a master chaotic signal. The modulated signal is subsequently recovered by a slave system, and the fast convergence to zero of the information recovery error substantiates the effectiveness of the design.}, journal={IEEE ACCESS}, author={Nwachioma, Christian and Humberto Perez-Cruz, J. and Jimenez, Abimael and Ezuma, Martins and Rivera-Blas, R.}, year={2019}, pages={7510–7521} }