Signal Processing in Wireless Communications: Device Fingerprinting and Wide-Band Interference Rejection

The rapid progress of wireless communication technologies that has taken place in recent years has significantly improved the quality of everyday life. However with this expansion of wireless communication systems come significant security threats and significant technological challenges, both of which are due to the fact that the communication medium is shared. The ubiquity of open wireless Internet access networks creates a new avenue for cyber-criminals to impersonate and act in an unauthorized way. The increasing number of deployed wide-band wireless communication systems entails technological challenges for effective utilization of the shared medium, which implies the need for advanced interference rejection methods. Wireless security and interference rejection in wide-band wireless communications are therefore often considered as the two main challenges in wireless network\u27s design and research. Important aspects of these challenges are illuminated and addressed in this dissertation. This dissertation considers signal processing approaches for exploiting or mitigating the effects of non-ideal components in wireless communication systems. In the first part of the dissertation, we introduce and study a novel, model-based approach to wireless device identification that exploits imperfections in the transmitter caused by manufacturing process nonidealities. Previous approaches to device identification based on hardware imperfections vary from transient analysis to machine learning but have not provided verifiable accuracy. Here, we detail a model-based approach, that uses statistical models of RF transmitter components: digital-to-analog converter, power amplifier and RF oscillator, which are amenable for analysis. Our proposed approach examines the key device characteristics that cause anonymity loss, countermeasures that can be applied by the nodes to regain the anonymity, and ways of thwarting such countermeasures. We develop identification algorithms based on statistical signal processing methods and address the challenging scenario when the units that need to be distinguished from one another are of the same model and from the same manufacturer. Using simulations and measurements of components that are commonly used in commercial communications systems, we show that our anonymity breaking techniques are effective. In the second part of the dissertation, we consider innovative approaches for the acquisition of frequency-sparse signals with wide-band receivers when a weak signal of interest is received in the presence of a very strong interference, and the effects of the nonlinearities in the low-noise amplifier at the receiver must be mitigated. All samples with amplitude above a given threshold, dictated by the linear input range of the receiver, are discarded to avoid the distortion caused by saturation of the low noise amplifier. Such a sampling scheme, while avoiding nonlinear distortion that cannot be corrected in the digital domain, poses challenges for signal reconstruction techniques, as the samples are taken non-uniformly, but also non-randomly. The considered approaches fall into the field of compressive sensing (CS); however, what differentiates them from conventional CS is that a structure is forced upon the measurement scheme. Such a structure causes a violation of the core CS assumption of the measurements\u27 randomness. We consider two different types of structured acquisition: signal independent and signal dependent structured acquisition. For the first case, we derive bounds on the number of samples needed for successful CS recovery when samples are drawn at random in predefined groups. For the second case, we consider enhancements of CS recovery methods when only small-amplitude samples of the signal that needs to be recovered are available for the recovery. Finally, we address a problem of spectral leakage due to the limited processing block size of block processing, wide-band receivers and propose an adaptive block size adjustment method, which leads to significant dynamic range improvements

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Examining Terrorism Attacks and Regional Preparedness in the United States

After the 9/11 terrorist attacks, the U.S. government renewed efforts to prepare for future attacks. Despite research on federal and state government preparedness, there was a lack of scholarship on trends in terrorist attacks at the local level. The purpose of this quantitative descriptive study was to examine trends in terrorism attacks in the United States between 2001 and 2018 and determine whether significance exists between characteristics of the terrorism incidents (weapon used, target type) and region. The conceptual framework included Grundmann’s risk management and Tomuzia et al.’s risk assessment scenario models. Answering the research questions entailed examining trends in terrorist attacks in the United States between 2001 and 2018 (number of incidents, injuries, fatalities), including relationships by region, weapon used, and target type. Secondary data from the Global Terrorism Database underwent analysis using ARIMA models for time-series data and chi-square and post hoc analyses for categorical level data. There was an examination of type of weapon used and target type for differences between regions. Findings revealed that trends in terrorist attacks for injuries did not differ across time; however, trends in terrorist attacks for fatalities decreased over time. Changes in terrorist attacks by region were significantly related to weapon used and target type. Findings may lead to positive social change by helping policymakers understand future targets and characteristics of terrorist attacks, potentially improving preparedness and thereby reducing injuries and death. Future research is needed to confirm and expand the findings, including studies on terrorist attacks against the United States on foreign soil, such as those directed at U.S. embassies

Neural malware detection

At the heart of today’s malware problem lies theoretically infinite diversity created by metamorphism. The majority of conventional machine learning techniques tackle the problem with the assumptions that a sufficiently large number of training samples exist and that the training set is independent and identically distributed. However, the lack of semantic features combined with the models under these wrong assumptions result largely in overfitting with many false positives against real world samples, resulting in systems being left vulnerable to various adversarial attacks. A key observation is that modern malware authors write a script that automatically generates an arbitrarily large number of diverse samples that share similar characteristics in program logic, which is a very cost-effective way to evade detection with minimum effort. Given that many malware campaigns follow this paradigm of economic malware manufacturing model, the samples within a campaign are likely to share coherent semantic characteristics. This opens up a possibility of one-to-many detection. Therefore, it is crucial to capture this non-linear metamorphic pattern unique to the campaign in order to detect these seemingly diverse but identically rooted variants. To address these issues, this dissertation proposes novel deep learning models, including generative static malware outbreak detection model, generative dynamic malware detection model using spatio-temporal isomorphic dynamic features, and instruction cognitive malware detection. A comparative study on metamorphic threats is also conducted as part of the thesis. Generative adversarial autoencoder (AAE) over convolutional network with global average pooling is introduced as a fundamental deep learning framework for malware detection, which captures highly complex non-linear metamorphism through translation invariancy and local variation insensitivity. Generative Adversarial Network (GAN) used as a part of the framework enables oneshot training where semantically isomorphic malware campaigns are identified by a single malware instance sampled from the very initial outbreak. This is a major innovation because, to the best of our knowledge, no approach has been found to this challenging training objective against the malware distribution that consists of a large number of very sparse groups artificially driven by arms race between attackers and defenders. In addition, we propose a novel method that extracts instruction cognitive representation from uninterpreted raw binary executables, which can be used for oneto- many malware detection via one-shot training against frequency spectrum of the Transformer’s encoded latent representation. The method works regardless of the presence of diverse malware variations while remaining resilient to adversarial attacks that mostly use random perturbation against raw binaries. Comprehensive performance analyses including mathematical formulations and experimental evaluations are provided, with the proposed deep learning framework for malware detection exhibiting a superior performance over conventional machine learning methods. The methods proposed in this thesis are applicable to a variety of threat environments here artificially formed sparse distributions arise at the cyber battle fronts.Doctor of Philosoph

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Terpenóides voláteis e norisoprenóides em C13 presentes em vinhos: desenvolvimento de métodos rápidos de análise e avaliação do potencial biológico dos sesquiterpenóides

Doutoramento em QuímicaVitis vinifera L., the most widely cultivated fruit crop in the world, was the starting point for the development of this PhD thesis. This subject was exploited following on two actual trends: i) the development of rapid, simple, and high sensitive methodologies with minimal sample handling; and ii) the valuation of natural products as a source of compounds with potential health benefits. The target group of compounds under study were the volatile terpenoids (mono and sesquiterpenoids) and C13 norisoprenoids, since they may present biological impact, either from the sensorial point of view, as regards to the wine aroma, or by the beneficial properties for the human health. Two novel methodologies for quantification of C13 norisoprenoids in wines were developed. The first methodology, a rapid method, was based on the headspace solid-phase microextraction combined with gas chromatography-quadrupole mass spectrometry operating at selected ion monitoring mode (HS-SPME/GC-qMS-SIM), using GC conditions that allowed obtaining a C13 norisoprenoid volatile signature. It does not require any pre-treatment of the sample, and the C13 norisoprenoid composition of the wine was evaluated based on the chromatographic profile and specific m/z fragments, without complete chromatographic separation of its components. The second methodology, used as reference method, was based on the HS-SPME/GC-qMS-SIM, allowing the GC conditions for an adequate chromatographic resolution of wine components. For quantification purposes, external calibration curves were constructed with β-ionone, with regression coefficient (r2) of 0.9968 (RSD 12.51 %) and 0.9940 (RSD of 1.08 %) for the rapid method and for the reference method, respectively. Low detection limits (1.57 and 1.10 μg L-1) were observed. These methodologies were applied to seventeen white and red table wines. Two vitispirane isomers (158-1529 L-1) and 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) (6.42-39.45 μg L-1) were quantified. The data obtained for vitispirane isomers and TDN using the two methods were highly correlated (r2 of 0.9756 and 0.9630, respectively). A rapid methodology for the establishment of the varietal volatile profile of Vitis vinifera L. cv. 'Fernão-Pires' (FP) white wines by headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (HS-SPME/GCxGC-TOFMS) was developed. Monovarietal wines from different harvests, Appellations, and producers were analysed. The study was focused on the volatiles that seem to be significant to the varietal character, such as mono and sesquiterpenic compounds, and C13 norisoprenoids. Two-dimensional chromatographic spaces containing the varietal compounds using the m/z fragments 93, 121, 161, 175 and 204 were established as follows: 1tR = 255-575 s, 2tR = 0,424-1,840 s, for monoterpenoids, 1tR = 555-685 s, 2tR = 0,528-0,856 s, for C13 norisoprenoids, and 1tR = 695-950 s, 2tR = 0,520-0,960 s, for sesquiterpenic compounds. For the three chemical groups under study, from a total of 170 compounds, 45 were determined in all wines, allowing defining the "varietal volatile profile" of FP wine. Among these compounds, 15 were detected for the first time in FP wines. This study proposes a HS-SPME/GCxGC-TOFMS based methodology combined with classification-reference sample to be used for rapid assessment of varietal volatile profile of wines. This approach is very useful to eliminate the majority of the non-terpenic and non-C13 norisoprenic compounds, allowing the definition of a two-dimensional chromatographic space containing these compounds, simplifying the data compared to the original data, and reducing the time of analysis. The presence of sesquiterpenic compounds in Vitis vinifera L. related products, to which are assigned several biological properties, prompted us to investigate the antioxidant, antiproliferative and hepatoprotective activities of some sesquiterpenic compounds. Firstly, the antiradical capacity of trans,trans-farnesol, cis-nerolidol, α-humulene and guaiazulene was evaluated using chemical (DPPH• and hydroxyl radicals) and biological (Caco-2 cells) models. Guaiazulene (IC50= 0.73 mM) was the sesquiterpene with higher scavenger capacity against DPPH•, while trans,trans-farnesol (IC50= 1.81 mM) and cis-nerolidol (IC50= 1.48 mM) were more active towards hydroxyl radicals. All compounds, with the exception of α-humulene, at non-cytotoxic levels (≤ 1 mM), were able to protect Caco-2 cells from oxidative stress induced by tert-butyl hydroperoxide. The activity of the compounds under study was also evaluated as antiproliferative agents. Guaiazulene and cis-nerolidol were able to more effectively arrest the cell cycle in the S-phase than trans,trans-farnesol and α-humulene, being the last almost inactive. The relative hepatoprotection effect of fifteen sesquiterpenic compounds, presenting different chemical structures and commonly found in plants and plant-derived foods and beverages, was assessed. Endogenous lipid peroxidation and induced lipid peroxidation with tert-butyl hydroperoxide were evaluated in liver homogenates from Wistar rats. With the exception of α-humulene, all the sesquiterpenic compounds under study (1 mM) were effective in reducing the malonaldehyde levels in both endogenous and induced lipid peroxidation up to 35% and 70%, respectively. The developed 3D-QSAR models, relating the hepatoprotection activity with molecular properties, showed good fit (R2LOO > 0.819) with good prediction power (Q2 > 0.950 and SDEP < 2%) for both models. A network of effects associated with structural and chemical features of sesquiterpenic compounds such as shape, branching, symmetry, and presence of electronegative fragments, can modulate the hepatoprotective activity observed for these compounds. In conclusion, this study allowed the development of rapid and in-depth methods for the assessment of varietal volatile compounds that might have a positive impact on sensorial and health attributes related to Vitis vinifera L. These approaches can be extended to the analysis of other related food matrices, including grapes and musts, among others. In addition, the results of in vitro assays open a perspective for the promising use of the sesquiterpenic compounds, with similar chemical structures such as those studied in the present work, as antioxidants, hepatoprotective and antiproliferative agents, which meets the current challenges related to diseases of modern civilization.O ponto de partida para o desenvolvimento da presente tese de doutoramento foi a Vitis vinifera L., que é o fruto mais cultivado a nível mundial. Este tema foi explorado tendo em consideração duas tendências atuais: i) o desenvolvimento de métodos rápidos, simples e com elevada sensibilidade, minimizando a manipulação da amostra e ii) a valorização de produtos naturais como fonte de compostos com potencial efeito benéfico para a saúde. Os compostos voláteis varietais, nomeadamente os mono e sesquiterpenóides e os norisoprenóides em C13, representam as famílias-alvo em estudo, uma vez que estes apresentam propriedades biológicas interessantes do ponto de vista sensorial, podendo contribuir para o aroma do vinho, e com potenciais benefícios para a saúde humana. Foram desenvolvidas duas novas metodologias para quantificação de compostos norisoprenóides em C13 em vinhos. A primeira metodologia, denominada de método rápido, baseou-se na combinação da microextracção em fase sólida em modo espaço de cabeça com cromatografia em fase gasosa-quadrupolo espectrometria de massa, em modo de monitorização de ião selecionado (HS-SPME/GC-qMS-SIM), utilizando condições cromatográficas que permitiram a obtenção do perfil volátil em norisoprenóides em C13.Este método não requer nenhum pré-tratamento da amostra e a composição do vinho em norisoprenóides em C13 é avaliada com base no perfil cromatográfico e fragmentos m/z específicos, sem a completa separação cromatográfica dos compostos. A segunda metodologia, usada como método de referência, foi muito similar à anterior. Neste caso, foram definidas as condições cromatográficas adequadas para que ocorresse separação dos vários componentes voláteis do vinho. Foram preparadas curvas de calibração externas usando a β-ionona como padrão, tendo-se obtido coeficientes de regressão (r2) de 0,9968 (RSD 12,51 %) e 0,9940 (RSD 1,08 %) para o método rápido e método de referência, respetivamente, com baixos limites de deteção (1,57e 1,10 μg L-1, respetivamente). Foram analisados 17 vinhos brancos e tintos, nos quais os isómeros do vitispirano e o TDN (1,1,6-trimetil-1,2-dihidronaftaleno) apresentaram concentrações entre 158 e1529 μg L-1 e entre 6,42 e 39,45 μg L-1, respetivamente. Os dados obtidos pelos dois métodos desenvolvidos para as concentrações dos isómeros do vitispirano e do TDN estão altamente correlacionados (r2 de 0,9756 e 0,9630, respetivamente). O perfil volátil varietal dos vinhos brancos da casta Portuguesa Vitis vinifera L. cv. Fernão-Pires (FP) foi estabelecido por um método rápido baseado na HS-SPME seguida de cromatografia em fase gasosa abrangente bidimensional (HS-SPME/GCxGC-TOFMS). Foram analisados vinhos monovarietais de diferentes colheitas, Regiões Demarcadas e produtores. O estudo focou-se em compostos voláteis que podem contribuir para o caráter varietal dos vinhos, nomeadamente os compostos mono e sesquiterpenóides e norisoprenóides em C13. Foram estabelecidos espaços cromatográficos bidimensionais específicos para as 3 famílias químicas em estudo, utilizando os fragmentos m/z 93, 121, 161, 175 e 204: para os monoterpenóides (1tR = 255-575 s, 2tR = 0,424-1,840 s), para os norisoprenóides em C13 (1tR = 555-685 s, 2tR = 0,528-0,856 s) e para os sesquiterpenóides (1tR = 695-950 s, 2tR = 0,520-0,960 s). Através da aplicação desta metodologia foram identificados um total de 170 compostos varietais em vinhos FP. Quarenta e cinco dos 170 compostos identificados são comuns a todos os vinhos da casta FP, permitindo assim estabelecer o perfil varietal para a casta. Destes, 15 compostos foram descritos pela primeira vez para esta casta. A combinação da metodologia de HS-SPME/GCxGC-TOFMS com o processamento de dados por utilização de uma amostra de classificação-referência permitiu a identificação rápida do perfil varietal de vinhos. Esta abordagem foi muito útil para eliminar a maioria dos compostos não-terpénicos e não norisoprenóides em C13 uma vez que: 1) permite definir um espaço cromatográfico bidimensional que contém estes compostos; 2) simplifica a complexidade e o número de dados obtidos comparativamente com os dados originais; e 3) reduz o tempo de análise. A presença de compostos sesquiterpénicos em produtos de Vitis vinifera L., aos quais são atribuídos vários efeitos benéficos para a saúde foi um fator motivador para o estudo de vários efeitos biológicos associados a estes compostos, nomeadamente das atividades antioxidante, antiproliferativa e hepatoprotetora. Inicialmente, foi avaliada a capacidade antioxidante do trans,trans-farnesol, cis-nerolidol, α-humuleno e o guaiazuleno usando modelos químicos (DPPH•, radical hidroxilo) e biológicos (células do tipo Caco-2). A avaliação da atividade anti-radicalar mostrou que o guaiazuleno inibe eficientemente o DPPH• (IC50 0,73), enquanto o trans,trans-farnesol (IC50= 1,81 mM) e o cis-nerolidol (IC50=1,48 mM) foram mais ativos em relação ao radical hidroxilo. A avaliação da atividade antioxidante em condições não citotóxicas (≤ 1 mM) revelou que todos os compostos, com exceção do α-humulene, foram capazes de proteger as células do tipo Caco-2 contra o stresse oxidativo induzido pelo hidroperóxido de tert-butilo. Os compostos foram também avaliados quanto à sua ação antiproliferativa. O guaiazuleno e o cis-nerolidol foram capazes de interromper o ciclo celular na fase da síntese do ADN mais efetivamente que o trans,trans-farnesol e o a-humuleno, sendo o último praticamente inativo. Foi ainda avaliada a capacidade hepatoprotetora relativa de 15 compostos sesquiterpenénicos com estruturas químicas diversas e comuns em plantas, alimentos e bebidas derivadas de plantas. A peroxidação lipídica endógena e a peroxidação lipídica induzida pelo hidroperóxido de tert-butilo foram avaliadas em homogeneizado de fígado de rato. Todos os compostos em estudo (1 mM), com exceção do a-humuleno, foram eficientes na redução dos níveis de malonaldeido na peroxidação lipídica endógena (35%) e na peroxidação induzida (70%). Foram desenvolvidos modelos 3D-QSAR, que relacionam a actividade hepatoprotetora com descritores moleculares, os quais apresentaram valores de calibração de R2LOO > 0,81) e poder de previsão de Q2LOO >0,97 e SDEP < 2%. Verifica-se que há uma rede de efeitos associados com caraterísticas estruturais e químicas dos compostos, tais como a configuração da molécula, maior ou menor grau de ramificação, simetria e a presença de fragmentos eletronegativos, que podem modular a atividade hepatoprotetora observada para estes compostos. Em conclusão, este estudo permitiu o desenvolvimento de métodos rápidos e que também fornecem informação detalhada sobre os compostos voláteis varietais, com potencial impacto positivo nas caraterísticas sensoriais e benéficas para a saúde relacionadas com a Vitis vinifera L. A aplicação deste tipo de metodologia pode ser alargada a outro tipo de matrizes, tais como uvas, mostos e muitos outros tipos de matrizes vegetais. Além disso, é ainda de realçar que os resultados bastante promissores dos ensaios in vitro abrem novas perspetivas ao uso dos compostos sesquiterpénicos, com estruturas químicas similares aos estudados no âmbito deste trabalho, como agentes antioxidantes, hepatoprotetores e antiproliferativos, o que está em linha com os desafios atuais relacionados com as doenças civilizacionais atuais

Cybersecurity of Digital Service Chains

This open access book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios. We expect the book to be interesting for the broad group of researchers, engineers, and professionals daily experiencing the inadequacy of outdated cybersecurity models for modern computing environments and cyber-physical systems

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov