Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Efficient Multiband Algorithms for Blind Source Separation

The problem of blind separation refers to recovering original signals, called source signals, from the mixed signals, called observation signals, in a reverberant environment. The mixture is a function of a sequence of original speech signals mixed in a reverberant room. The objective is to separate mixed signals to obtain the original signals without degradation and without prior information of the features of the sources. The strategy used to achieve this objective is to use multiple bands that work at a lower rate, have less computational cost and a quicker convergence than the conventional scheme. Our motivation is the competitive results of unequal-passbands scheme applications, in terms of the convergence speed. The objective of this research is to improve unequal-passbands schemes by improving the speed of convergence and reducing the computational cost. The first proposed work is a novel maximally decimated unequal-passbands scheme.This scheme uses multiple bands that make it work at a reduced sampling rate, and low computational cost. An adaptation approach is derived with an adaptation step that improved the convergence speed. The performance of the proposed scheme was measured in different ways. First, the mean square errors of various bands are measured and the results are compared to a maximally decimated equal-passbands scheme, which is currently the best performing method. The results show that the proposed scheme has a faster convergence rate than the maximally decimated equal-passbands scheme. Second, when the scheme is tested for white and coloured inputs using a low number of bands, it does not yield good results; but when the number of bands is increased, the speed of convergence is enhanced. Third, the scheme is tested for quick changes. It is shown that the performance of the proposed scheme is similar to that of the equal-passbands scheme. Fourth, the scheme is also tested in a stationary state. The experimental results confirm the theoretical work. For more challenging scenarios, an unequal-passbands scheme with over-sampled decimation is proposed; the greater number of bands, the more efficient the separation. The results are compared to the currently best performing method. Second, an experimental comparison is made between the proposed multiband scheme and the conventional scheme. The results show that the convergence speed and the signal-to-interference ratio of the proposed scheme are higher than that of the conventional scheme, and the computation cost is lower than that of the conventional scheme

Some Results on Distinguishing Attacks on Stream Ciphers

Stream ciphers are cryptographic primitives that are used to ensure the privacy of a message that is sent over a digital communication channel. In this thesis we will present new cryptanalytic results for several stream ciphers. The thesis provides a general introduction to cryptology, explains the basic concepts, gives an overview of various cryptographic primitives and discusses a number of different attack models. The first new attack given is a linear correlation attack in the form of a distinguishing attack. In this attack a specific class of weak feedback polynomials for LFSRs is identified. If the feedback polynomial is of a particular form the attack will be efficient. Two new distinguishing attacks are given on classical stream cipher constructions, namely the filter generator and the irregularly clocked filter generator. It is also demonstrated how these attacks can be applied to modern constructions. A key recovery attack is described for LILI-128 and a distinguishing attack for LILI-II is given. The European network of excellence, called eSTREAM, is an effort to find new efficient and secure stream ciphers. We analyze a number of the eSTREAM candidates. Firstly, distinguishing attacks are described for the candidate Dragon and a family of candidates called Pomaranch. Secondly, we describe resynchronization attacks on eSTREAM candidates. A general square root resynchronization attack which can be used to recover parts of a message is given. The attack is demonstrated on the candidates LEX and Pomaranch. A chosen IV distinguishing attack is then presented which can be used to evaluate the initialization procedure of stream ciphers. The technique is demonstrated on four candidates: Grain, Trivium, Decim and LEX

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

On the Design and Analysis of Stream Ciphers

This thesis presents new cryptanalysis results for several different stream cipher constructions. In addition, it also presents two new stream ciphers, both based on the same design principle. The first attack is a general attack targeting a nonlinear combiner. A new class of weak feedback polynomials for linear feedback shift registers is identified. By taking samples corresponding to the linear recurrence relation, it is shown that if the feedback polynomial has taps close together an adversary to take advantage of this by considering the samples in a vector form. Next, the self-shrinking generator and the bit-search generator are analyzed. Both designs are based on irregular decimation. For the self-shrinking generator, it is shown how to recover the internal state knowing only a few keystream bits. The complexity of the attack is similar to the previously best known but uses a negligible amount of memory. An attack requiring a large keystream segment is also presented. It is shown to be asymptotically better than all previously known attacks. For the bit-search generator, an algorithm that recovers the internal state is given as well as a distinguishing attack that can be very efficient if the feedback polynomial is not carefully chosen. Following this, two recently proposed stream cipher designs, Pomaranch and Achterbahn, are analyzed. Both stream ciphers are designed with small hardware complexity in mind. For Pomaranch Version 2, based on an improvement of previous analysis of the design idea, a key recovery attack is given. Also, for all three versions of Pomaranch, a distinguishing attack is given. For Achterbahn, it is shown how to recover the key of the latest version, known as Achterbahn-128/80. The last part of the thesis introduces two new stream cipher designs, namely Grain and Grain-128. The ciphers are designed to be very small in hardware. They also have the distinguishing feature of allowing users to increase the speed of the ciphers by adding extra hardware

Probabilistic modeling of wavelet coefficients for processing of image and video signals

Statistical estimation and detection techniques are widely used in signal processing including wavelet-based image and video processing. The probability density function (PDF) of the wavelet coefficients of image and video signals plays a key role in the development of techniques for such a processing. Due to the fixed number of parameters, the conventional PDFs for the estimators and detectors usually ignore higher-order moments. Consequently, estimators and detectors designed using such PDFs do not provide a satisfactory performance. This thesis is concerned with first developing a probabilistic model that is capable of incorporating an appropriate number of parameters that depend on higher-order moments of the wavelet coefficients. This model is then used as the prior to propose certain estimation and detection techniques for denoising and watermarking of image and video signals. Towards developing the probabilistic model, the Gauss-Hermite series expansion is chosen, since the wavelet coefficients have non-compact support and their empirical density function shows a resemblance to the standard Gaussian function. A modification is introduced in the series expansion so that only a finite number of terms can be used for modeling the wavelet coefficients with rendering the resulting PDF to become negative. The parameters of the resulting PDF, called the modified Gauss-Hermite (NIGH) PDF, are evaluated in terms of the higher-order sample-moments. It is shown that the MGH PDF fits the empirical density function better than the existing PDFs that use a limited number of parameters do. The proposed MGH PDF is used as the prior of image and video signals in designing maximum a posteriori and minimum mean squared error-based estimators for denoising of image and video signals and log-likelihood ratio-based detector for watermarking of image signals. The performance of the estimation and detection techniques are then evaluated in terms of the commonly used metrics. It is shown through extensive experimentations that the estimation and detection techniques developed utilizing the proposed MGH PDF perform substantially better than those that utilize the conventional PDFs. These results confirm that the superior fit of the MGH PDF to the empirical density function resulting from the flexibility of the MGH PDF in choosing the number of parameters, which are functions of higher-order moments of data, leads to the better performance. Thus, the proposed MGH PDF should play a significant role in wavelet-based image and video signal processin

Apprentissage automatique pour le codage cognitif de la parole

Depuis les années 80, les codecs vocaux reposent sur des stratégies de codage à court terme qui fonctionnent au niveau de la sous-trame ou de la trame (généralement 5 à 20 ms). Les chercheurs ont essentiellement ajusté et combiné un nombre limité de technologies disponibles (transformation, prédiction linéaire, quantification) et de stratégies (suivi de forme d'onde, mise en forme du bruit) pour construire des architectures de codage de plus en plus complexes. Dans cette thèse, plutôt que de s'appuyer sur des stratégies de codage à court terme, nous développons un cadre alternatif pour la compression de la parole en codant les attributs de la parole qui sont des caractéristiques perceptuellement importantes des signaux vocaux. Afin d'atteindre cet objectif, nous résolvons trois problèmes de complexité croissante, à savoir la classification, la prédiction et l'apprentissage des représentations. La classification est un élément courant dans les conceptions de codecs modernes. Dans un premier temps, nous concevons un classifieur pour identifier les émotions, qui sont parmi les attributs à long terme les plus complexes de la parole. Dans une deuxième étape, nous concevons un prédicteur d'échantillon de parole, qui est un autre élément commun dans les conceptions de codecs modernes, pour mettre en évidence les avantages du traitement du signal de parole à long terme et non linéaire. Ensuite, nous explorons les variables latentes, un espace de représentations de la parole, pour coder les attributs de la parole à court et à long terme. Enfin, nous proposons un réseau décodeur pour synthétiser les signaux de parole à partir de ces représentations, ce qui constitue notre dernière étape vers la construction d'une méthode complète de compression de la parole basée sur l'apprentissage automatique de bout en bout. Bien que chaque étape de développement proposée dans cette thèse puisse faire partie d'un codec à elle seule, chaque étape fournit également des informations et une base pour la prochaine étape de développement jusqu'à ce qu'un codec entièrement basé sur l'apprentissage automatique soit atteint. Les deux premières étapes, la classification et la prédiction, fournissent de nouveaux outils qui pourraient remplacer et améliorer des éléments des codecs existants. Dans la première étape, nous utilisons une combinaison de modèle source-filtre et de machine à état liquide (LSM), pour démontrer que les caractéristiques liées aux émotions peuvent être facilement extraites et classées à l'aide d'un simple classificateur. Dans la deuxième étape, un seul réseau de bout en bout utilisant une longue mémoire à court terme (LSTM) est utilisé pour produire des trames vocales avec une qualité subjective élevée pour les applications de masquage de perte de paquets (PLC). Dans les dernières étapes, nous nous appuyons sur les résultats des étapes précédentes pour concevoir un codec entièrement basé sur l'apprentissage automatique. un réseau d'encodage, formulé à l'aide d'un réseau neuronal profond (DNN) et entraîné sur plusieurs bases de données publiques, extrait et encode les représentations de la parole en utilisant la prédiction dans un espace latent. Une approche d'apprentissage non supervisé basée sur plusieurs principes de cognition est proposée pour extraire des représentations à partir de trames de parole courtes et longues en utilisant l'information mutuelle et la perte contrastive. La capacité de ces représentations apprises à capturer divers attributs de la parole à court et à long terme est démontrée. Enfin, une structure de décodage est proposée pour synthétiser des signaux de parole à partir de ces représentations. L'entraînement contradictoire est utilisé comme une approximation des mesures subjectives de la qualité de la parole afin de synthétiser des échantillons de parole à consonance naturelle. La haute qualité perceptuelle de la parole synthétisée ainsi obtenue prouve que les représentations extraites sont efficaces pour préserver toutes sortes d'attributs de la parole et donc qu'une méthode de compression complète est démontrée avec l'approche proposée.Abstract: Since the 80s, speech codecs have relied on short-term coding strategies that operate at the subframe or frame level (typically 5 to 20ms). Researchers essentially adjusted and combined a limited number of available technologies (transform, linear prediction, quantization) and strategies (waveform matching, noise shaping) to build increasingly complex coding architectures. In this thesis, rather than relying on short-term coding strategies, we develop an alternative framework for speech compression by encoding speech attributes that are perceptually important characteristics of speech signals. In order to achieve this objective, we solve three problems of increasing complexity, namely classification, prediction and representation learning. Classification is a common element in modern codec designs. In a first step, we design a classifier to identify emotions, which are among the most complex long-term speech attributes. In a second step, we design a speech sample predictor, which is another common element in modern codec designs, to highlight the benefits of long-term and non-linear speech signal processing. Then, we explore latent variables, a space of speech representations, to encode both short-term and long-term speech attributes. Lastly, we propose a decoder network to synthesize speech signals from these representations, which constitutes our final step towards building a complete, end-to-end machine-learning based speech compression method. The first two steps, classification and prediction, provide new tools that could replace and improve elements of existing codecs. In the first step, we use a combination of source-filter model and liquid state machine (LSM), to demonstrate that features related to emotions can be easily extracted and classified using a simple classifier. In the second step, a single end-to-end network using long short-term memory (LSTM) is shown to produce speech frames with high subjective quality for packet loss concealment (PLC) applications. In the last steps, we build upon the results of previous steps to design a fully machine learning-based codec. An encoder network, formulated using a deep neural network (DNN) and trained on multiple public databases, extracts and encodes speech representations using prediction in a latent space. An unsupervised learning approach based on several principles of cognition is proposed to extract representations from both short and long frames of data using mutual information and contrastive loss. The ability of these learned representations to capture various short- and long-term speech attributes is demonstrated. Finally, a decoder structure is proposed to synthesize speech signals from these representations. Adversarial training is used as an approximation to subjective speech quality measures in order to synthesize natural-sounding speech samples. The high perceptual quality of synthesized speech thus achieved proves that the extracted representations are efficient at preserving all sorts of speech attributes and therefore that a complete compression method is demonstrated with the proposed approach

Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite Systems

Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite SystemsNowadays, the operation of global navigation satellite systems (GNSS) is imperative across a multitude of applications worldwide. The increasing reliance on accurate positioning and timing information has made more serious than ever the consequences of possible service outages in the satellite navigation systems. Among others, interference is regarded as the primary threat to their operation. Due the recent proliferation of portable interferers, notably jammers, it has now become common for GNSS receivers to endure simultaneous attacks from multiple sources of interference, which are likely spatially distributed and transmit different modulations. To the best knowledge of the author, the present dissertation is the first publication to investigate the use of the S-transform (ST) to devise countermeasures to interference. The original contributions in this context are mainly: • the formulation of a complexity-scalable ST implementable in real time as a bank of filters; • a method for characterizing and localizing multiple in-car jammers through interference snapshots that are collected by separate receivers and analysed with a clever use of the ST; • a preliminary assessment of novel methods for mitigating generic interference at the receiver end by means the ST and more computationally efficient variants of the transform. Besides GNSSs, the countermeasures to interference proposed are equivalently applicable to protect any direct-sequence spread spectrum (DS-SS) communication

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Design and Analysis of Security Schemes for Low-cost RFID Systems

With the remarkable progress in microelectronics and low-power semiconductor technologies, Radio Frequency IDentification technology (RFID) has moved from obscurity into mainstream applications, which essentially provides an indispensable foundation to realize ubiquitous computing and machine perception. However, the catching and exclusive characteristics of RFID systems introduce growing security and privacy concerns. To address these issues are particularly challenging for low-cost RFID systems, where tags are extremely constrained in resources, power and cost. The primary reasons are: (1) the security requirements of low-cost RFID systems are even more rigorous due to large operation range and mass deployment; and (2) the passive tags' modest capabilities and the necessity to keep their prices low present a novel problem that goes beyond the well-studied problems of traditional cryptography. This thesis presents our research results on the design and the analysis of security schemes for low-cost RFID systems. Motivated by the recent attention on exploiting physical layer resources in the design of security schemes, we investigate how to solve the eavesdropping, modification and one particular type of relay attacks toward the tag-to-reader communication in passive RFID systems without requiring lightweight ciphers. To this end, we propose a novel physical layer scheme, called Backscatter modulation- and Uncoordinated frequency hopping-assisted Physical Layer Enhancement (BUPLE). The idea behind it is to use the amplitude of the carrier to transmit messages as normal, while to utilize its periodically varied frequency to hide the transmission from the eavesdropper/relayer and to exploit a random sequence modulated to the carrier's phase to defeat malicious modifications. We further improve its eavesdropping resistance through the coding in the physical layer, since BUPLE ensures that the tag-to-eavesdropper channel is strictly noisier than the tag-to-reader channel. Three practical Wiretap Channel Codes (WCCs) for passive tags are then proposed: two of them are constructed from linear error correcting codes, and the other one is constructed from a resilient vector Boolean function. The security and usability of BUPLE in conjunction with WCCs are further confirmed by our proof-of-concept implementation and testing. Eavesdropping the communication between a legitimate reader and a victim tag to obtain raw data is a basic tool for the adversary. However, given the fundamentality of eavesdropping attacks, there are limited prior work investigating its intension and extension for passive RFID systems. To this end, we firstly identified a brand-new attack, working at physical layer, against backscattered RFID communications, called unidirectional active eavesdropping, which defeats the customary impression that eavesdropping is a ``passive" attack. To launch this attack, the adversary transmits an un-modulated carrier (called blank carrier) at a certain frequency while a valid reader and a tag interacts at another frequency channel. Once the tag modulates the amplitude of reader's signal, it causes fluctuations on the blank carrier as well. By carefully examining the amplitude of the backscattered versions of the blank carrier and the reader's carrier, the adversary could intercept the ongoing reader-tag communication with either significantly lower bit error rate or from a significantly greater distance away. Our concept is demonstrated and empirically analyzed towards a popular low-cost RFID system, i.e., EPC Gen2. Although active eavesdropping in general is not trivial to be prohibited, for a particular type of active eavesdropper, namely a greedy proactive eavesdropper, we propose a simple countermeasure without introducing extra cost to current RFID systems. The needs of cryptographic primitives on constraint devices keep increasing with the growing pervasiveness of these devices. One recent design of the lightweight block cipher is Hummingbird-2. We study its cryptographic strength under a novel technique we developed, called Differential Sequence Attack (DSA), and present the first cryptanalytic result on this cipher. In particular, our full attack can be divided into two phases: preparation phase and key recovery phase. During the key recovery phase, we exploit the fact that the differential sequence for the last round of Hummingbird-2 can be retrieved by querying the full cipher, due to which, the search space of the secret key can be significantly reduced. Thus, by attacking the encryption (decryption resp.) of Hummingbird-2, our algorithm recovers 36-bit (another 28-bit resp.) out of 128-bit key with $2^{68}$ ($2^{60}$ resp.) time complexity if particular differential conditions of the internal states and of the keys at one round can be imposed. Additionally, the rest 64-bit of the key can be exhaustively searched and the overall time complexity is dominated by $2^{68}$. During the preparation phase, by investing $2^{81}$ effort in time, the adversary is able to create the differential conditions required in the key recovery phase with at least 0.5 probability. As an additional effort, we examine the cryptanalytic strength of another lightweight candidate known as A2U2, which is the most lightweight cryptographic primitive proposed so far for low-cost tags. Our chosen-plaintext-attack fully breaks this cipher by recovering its secret key with only querying the encryption twice on the victim tag and solving 32 sparse systems of linear equations (where each system has 56 unknowns and around 28 unknowns can be directly obtained without computation) in the worst case, which takes around 0.16 second on a Thinkpad T410 laptop