arXiv subjects

Christophe Guyeux

Publications and source records attributed to Christophe Guyeux.

At least 55 records · Page 3Linked to original sources

arXiv2016

Investigating Low Level Protocols for Wireless Body Sensor Networks

The rapid development of medical sensors has increased the interest in Wireless Body Area Network (WBAN) applications where physiological data from the human body and its environment is gathered, monitored, and analyzed to take the proper measures. In WBANs, it is essential to design MAC protocols that ensure adequate Quality of Service (QoS) such as low delay and high scalability. This paper investigates Medium Access Control (MAC) protocols used in WBAN, and compares their performance in a high traffic environment. Such scenario can be induced in case of emergency for example, where physiological data collected from all sensors on human body should be sent simultaneously to take appropriate action. This study can also be extended to cover collaborative WBAN systems where information from different bodies is sent simultaneously leading to high traffic. OPNET simulations are performed to compare the delay and scalability performance of the different MAC protocols under the same experimental conditions and to draw conclusions about the best protocol to be used in a high traffic environment.

cs.DC↗

arXiv2016

FPGA Implementation of $\mathbb{F}_2$-Linear Pseudorandom Number Generators Based on Zynq MPSoC: a Chaotic Iterations Post Processing Case Study

Pseudorandom number generation (PRNG) is a key element in hardware security platforms like field-programmable gate array FPGA circuits. In this article, 18 PRNGs belonging in 4 families (xorshift, LFSR, TGFSR, and LCG) are physically implemented in a FPGA and compared in terms of area, throughput, and statistical tests. Two flows of conception are used for Register Transfer Level (RTL) and High-level Synthesis (HLS). Additionally, the relations between linear complexity, seeds, and arithmetic operations on the one hand, and the resources deployed in FPGA on the other hand, are deeply investigated. In order to do that, a SoC based on Zynq EPP with ARM Cortex-$A9$ MPSoC is developed to accelerate the implementation and the tests of various PRNGs on FPGA hardware. A case study is finally proposed using chaotic iterations as a post processing for FPGA. The latter has improved the statistical profile of a combination of PRNGs that, without it, failed in the so-called TestU01 statistical battery of tests.

cs.CR↗

arXiv2016

On the Evaluation of the Privacy Breach in Disassociated Set-Valued Datasets

Data anonymization is gaining much attention these days as it provides the fundamental requirements to safely outsource datasets containing identifying information. While some techniques add noise to protect privacy others use generalization to hide the link between sensitive and non-sensitive information or separate the dataset into clusters to gain more utility. In the latter, often referred to as bucketization, data values are kept intact, only the link is hidden to maximize the utility. In this paper, we showcase the limits of disassociation, a bucketization technique that divides a set-valued dataset into $k^m$-anonymous clusters. We demonstrate that a privacy breach might occur if the disassociated dataset is subject to a cover problem. We finally evaluate the privacy breach using the quantitative privacy breach detection algorithm on real disassociated datasets.

cs.CR↗

arXiv2016

Randomness and disorder of chaotic iterations. Applications in information security field

Design and cryptanalysis of chaotic encryption schemes are major concerns to provide secured information systems. Pursuing our previous research works, some well-defined discrete chaotic iterations that satisfy the reputed Devaney's definition of chaos have been proposed. In this article, we summarize these contributions and propose applications in the fields of pseudorandom number generation, hash functions, and symmetric cryptography. For all these applications, the proofs of chaotic properties are outlined.

nlin.CD↗

arXiv2016

A clustering tool for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Models

We propose a new procedure for clustering nucleotide sequences based on the "Laplacian Eigenmaps" and Gaussian Mixture modelling. This proposal is then applied to a set of 100 DNA sequences from the mitochondrially encoded NADH dehydrogenase 3 (ND3) gene of a collection of Platyhelminthes and Nematoda species. The resulting clusters are then shown to be consistent with the gene phylogenetic tree computed using a maximum likelihood approach. This comparison shows in particular that the clustering produced by the methodology combining Laplacian Eigenmaps with Gaussian Mixture models is coherent with the phylogeny as well as with the NCBI taxonomy. We also developed a Python package for this procedure which is available online.

q-bio.QM↗

arXiv2016

Theoretical Design and FPGA-Based Implementation of Higher-Dimensional Digital Chaotic Systems

Traditionally, chaotic systems are built on the domain of infinite precision in mathematics. However, the quantization is inevitable for any digital devices, which causes dynamical degradation. To cope with this problem, many methods were proposed, such as perturbing chaotic states and cascading multiple chaotic systems. This paper aims at developing a novel methodology to design the higher-dimensional digital chaotic systems (HDDCS) in the domain of finite precision. The proposed system is based on the chaos generation strategy controlled by random sequences. It is proven to satisfy the Devaney's definition of chaos. Also, we calculate the Lyapunov exponents for HDDCS. The application of HDDCS in image encryption is demonstrated via FPGA platform. As each operation of HDDCS is executed in the same fixed precision, no quantization loss occurs. Therefore, it provides a perfect solution to the dynamical degradation of digital chaos.

nlin.CD↗

arXiv2016

Relation between Gene Content and Taxonomy in Chloroplasts

The aim of this study is to investigate the relation that can be found between the phylogeny of a large set of complete chloroplast genomes, and the evolution of gene content inside these sequences. Core and pan genomes have been computed on \textit{de novo} annotation of these 845 genomes, the former being used for producing well-supported phylogenetic tree while the latter provides information regarding the evolution of gene contents over time. It details too the specificity of some branches of the tree, when specificity is obtained on accessory genes. After having detailed the material and methods, we emphasize some remarkable relation between well-known events of the chloroplast history, like endosymbiosis, and the evolution of gene contents over the phylogenetic tree.

q-bio.GN↗

arXiv2016

Binary Particle Swarm Optimization versus Hybrid Genetic Algorithm for Inferring Well Supported Phylogenetic Trees

The amount of completely sequenced chloroplast genomes increases rapidly every day, leading to the possibility to build large-scale phylogenetic trees of plant species. Considering a subset of close plant species defined according to their chloroplasts, the phylogenetic tree that can be inferred by their core genes is not necessarily well supported, due to the possible occurrence of problematic genes (i.e., homoplasy, incomplete lineage sorting, horizontal gene transfers, etc.) which may blur the phylogenetic signal. However, a trustworthy phylogenetic tree can still be obtained provided such a number of blurring genes is reduced. The problem is thus to determine the largest subset of core genes that produces the best-supported tree. To discard problematic genes and due to the overwhelming number of possible combinations, this article focuses on how to extract the largest subset of sequences in order to obtain the most supported species tree. Due to computational complexity, a distributed Binary Particle Swarm Optimization (BPSO) is proposed in sequential and distributed fashions. Obtained results from both versions of the BPSO are compared with those computed using an hybrid approach embedding both genetic algorithms and statistical tests. The proposal has been applied to different cases of plant families, leading to encouraging results for these families.

cs.AI↗

arXiv2016

Using an epidemiological approach to maximize data survival in the internet of things

The internet of things (IoT) has gained worldwide attention in recent years. It transforms the everyday objects that surround us into proactive actors of the Internet, generating and consuming information. An important issue related to the appearance of such large-scale self-coordinating IoT is the reliability and the collaboration between the objects in the presence of environmental hazards. High failure rates lead to significant loss of data. Therefore, data survivability is a main challenge of the IoT. In this paper, we have developed a compartmental e-Epidemic SIR (Susceptible-Infectious-Recovered) model to save the data in the network and let it survive after attacks. Furthermore, our model takes into account the dynamic topology of the network where natural death (crashing nodes) and birth are defined and analyzed. Theoretical methods and simulations are employed to solve and simulate the system of equations developed and to analyze the model.

cs.DC↗

arXiv2016

Relaxing the Hypotheses of Symmetry and Time-Reversibility in Genome Evolutionary Models

Various genome evolutionary models have been proposed these last decades to predict the evolution of a DNA sequence over time, essentially described using a mutation matrix. By essence, all of these models relate the evolution of DNA sequences to the computation of the successive powers of the mutation matrix. To make this computation possible, hypotheses are assumed for the matrix, such as symmetry and time-reversibility, which are not compatible with mutation rates that have been recently obtained experimentally on genes ura3 and can1 of the Yeast Saccharomyces cerevisiae. In this work, authors investigate systematically the possibility to relax either the symmetry or the time-reversibility hypothesis of the mutation matrix, by investigating all the possible matrices of size 2*2 and 3*3. As an application example, the experimental study on the Yeast Saccharomyces cerevisiae has been used in order to deduce a simple mutation matrix, and to compute the future evolution of the rate purine/pyrimidine for $ura3$ on the one hand, and of the particular behavior of cytosines and thymines compared to purines on the other hand.

q-bio.BM↗

arXiv2016

Neural Networks and Chaos: Construction, Evaluation of Chaotic Networks, and Prediction of Chaos with Multilayer Feedforward Networks

Many research works deal with chaotic neural networks for various fields of application. Unfortunately, up to now these networks are usually claimed to be chaotic without any mathematical proof. The purpose of this paper is to establish, based on a rigorous theoretical framework, an equivalence between chaotic iterations according to Devaney and a particular class of neural networks. On the one hand we show how to build such a network, on the other hand we provide a method to check if a neural network is a chaotic one. Finally, the ability of classical feedforward multilayer perceptrons to learn sets of data obtained from a dynamical system is regarded. Various Boolean functions are iterated on finite states. Iterations of some of them are proven to be chaotic as it is defined by Devaney. In that context, important differences occur in the training process, establishing with various neural networks that chaotic behaviors are far more difficult to learn.

cs.NE↗

arXiv2016

A Topological Study of Chaotic Iterations. Application to Hash Functions

Chaotic iterations, a tool formerly used in distributed computing, has recently revealed various interesting properties of disorder leading to its use in the computer science security field. In this paper, a comprehensive study of its topological behavior is proposed. It is stated that, in addition to being chaotic as defined in the Devaney's formulation, this tool possesses the property of topological mixing. Additionally, its level of sensibility, expansivity, and topological entropy are evaluated. All of these properties lead to a complete unpredictable behavior for the chaotic iterations. As it only manipulates binary digits or integers, we show that it is possible to use it to produce truly chaotic computer programs. As an application example, a truly chaotic hash function is proposed in two versions. In the second version, an artificial neural network is used, which can be stated as chaotic according to Devaney.

nlin.CD↗

arXiv2016

Quality Analysis of a Chaotic Proven Keyed Hash Function

Hash functions are cryptographic tools, which are notably involved in integrity checking and password storage. They are of primary importance to improve the security of exchanges through the Internet. However, as security flaws have been recently identified in the current standard in this domain, new ways to hash digital data must be investigated. In this document an original keyed hash function is evaluated. It is based on asynchronous iterations leading to functions that have been proven to be chaotic. It thus possesses various topological properties as uniformity and sensibility to its initial condition. These properties make our hash function satisfies established security requirements in this field. This claim is qualitatively proven and experimentally verified in this research work, among other things by realizing a large number of simulations.

cs.CR↗

arXiv2016

FPGA Design for Pseudorandom Number Generator Based on Chaotic Iteration used in Information Hiding Application

Lots of researches indicate that the inefficient generation of random numbers is a significant bottleneck for information communication applications. Therefore, Field Programmable Gate Array (FPGA) is developed to process a scalable fixed-point method for random streams generation. In our previous researches, we have proposed a technique by applying some well-defined discrete chaotic iterations that satisfy the reputed Devaney's definition of chaos, namely chaotic iterations (CI). We have formerly proven that the generator with CI can provide qualified chaotic random numbers. In this paper, this generator based on chaotic iterations is optimally redesigned for FPGA device. By doing so, the generation rate can be largely improved. Analyses show that these hardware generators can also provide good statistical chaotic random bits and can be cryptographically secure too. An application in the information hiding security field is finally given as an illustrative example.

cs.CR↗

arXiv2016

Two Security Layers for Hierarchical Data Aggregation in Sensor Networks

Due to resource restricted sensor nodes, it is important to minimize the amount of data transmission among sensor networks. To reduce the amount of sending data, an aggregation approach can be applied along the path from sensors to the sink. However, as sensor networks are often deployed in untrusted and even hostile environments, sensor nodes are prone to node compromise attacks. Hence, an end-to-end secure aggregation approach is required to ensure a healthy data reception. In this paper, we propose two layers for secure data aggregation in sensor networks. Firstly, we provide an end-to-end encryption scheme that supports operations over cypher-text. It is based on elliptic curve cryptography that exploits a smaller key size, allows the use of higher number of operations on cypher-texts, and prevents the distinction between two identical texts from their cryptograms. Secondly, we propose a new watermarking-based authentication that enables sensor nodes to ensure the identity of other nodes they are communicating with. Our experiments show that our hybrid approach of secure data aggregation enhances the security, significantly reduces computation and communication overhead, and can be practically implemented in on-the-shelf sensor platforms.

cs.DC↗

arXiv2016

Epidemiological Approach for Data Survivability in Unattended Wireless Sensor Networks

Unattended Wireless Sensor Networks (UWSNs) are Wireless Sensor Networks characterized by sporadic sink presence and operation in hostile settings. The absence of the sink for period of time, prevents sensor nodes to offload data in real time and offer greatly increased opportunities for attacks resulting in erasure, modification, or disclosure of sensor-collected data. In this paper, we focus on UWSNs where sensor nodes collect and store data locally and try to upload all the information once the sink becomes available. One of the most relevant issues pertaining UWSNs is to guarantee a certain level of information survivability in an unreliable network and even in presence of a powerful attackers. In this paper, we first introduce an epidemic-domain inspired approach to model the information survivability in UWSN. Next, we derive a fully distributed algorithm that supports these models and give the correctness proofs.

cs.DC↗

arXiv2016

Theoretical design and circuit implementation of integer domain chaotic systems

In this paper, a new approach for constructing integer domain chaotic systems (IDCS) is proposed, and its chaotic behavior is mathematically proven according to the Devaney's definition of chaos. Furthermore, an analog-digital hybrid circuit is also developed for realizing the designed basic IDCS. In the IDCS circuit design, chaos generation strategy is realized through a sample-hold circuit and a decoder circuit so as to convert the uniform noise signal into a random sequence, which plays a key role in circuit implementation. The experimental observations further validate the proposed systematic methodology for the first time.

nlin.CD↗

arXiv2016

Resiliency in Distributed Sensor Networks for PHM of the Monitoring Targets

In condition-based maintenance, real-time observations are crucial for on-line health assessment. When the monitoring system is a wireless sensor network, data loss becomes highly probable and this affects the quality of the remaining useful life prediction. In this paper, we present a fully distributed algorithm that ensures fault tolerance and recovers data loss in wireless sensor networks. We first theoretically analyze the algorithm and give correctness proofs, then provide simulation results and show that the algorithm is (i) able to ensure data recovery with a low failure rate and (ii) preserves the overall energy for dense networks.

cs.DC↗