Search results for "Pseudorandom number generator"

showing 10 items of 17 documents

A Hardware and Secure Pseudorandom Generator for Constrained Devices

2018

Hardware security for an Internet of Things or cyber physical system drives the need for ubiquitous cryptography to different sensing infrastructures in these fields. In particular, generating strong cryptographic keys on such resource-constrained device depends on a lightweight and cryptographically secure random number generator. In this research work, we have introduced a new hardware chaos-based pseudorandom number generator, which is mainly based on the deletion of an Hamilton cycle within the $N$ -cube (or on the vectorial negation), plus one single permutation. We have rigorously proven the chaotic behavior and cryptographically secure property of the whole proposal: the mid-term eff…

Applied cryptography; Chaotic circuits; Constrained devices; Discrete dynamical systems; FPGA; Lightweight Cryptography; Random number generators; Statistical tests; Control and Systems Engineering; Information Systems; Computer Science Applications1707 Computer Vision and Pattern Recognition; Electrical and Electronic EngineeringHardware security moduleComputer scienceRandom number generationCryptography[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]02 engineering and technologyPseudorandom generatorConstrained devicesLightweight CryptographyChaotic circuits[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]PermutationRandom number generatorsStatistical tests0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringField-programmable gate arrayThroughput (business)FPGAPseudorandom number generatorGenerator (category theory)business.industry020208 electrical & electronic engineeringComputer Science Applications1707 Computer Vision and Pattern Recognition020206 networking & telecommunicationsDiscrete dynamical systems[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationComputer Science ApplicationsApplied cryptography[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Control and Systems EngineeringKey (cryptography)[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]businessComputer hardwareInformation SystemsIEEE Transactions on Industrial Informatics
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Construction of pseudo-random sequences from chaos

2002

CHAOS (operating system)Pseudorandom number generatorTheoretical computer scienceRandom number generationbusiness.industryTelecommunication securityCryptographybusinessMathematics2000 2nd International Conference. Control of Oscillations and Chaos. Proceedings (Cat. No.00TH8521)
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Random Slicing: Efficient and Scalable Data Placement for Large-Scale Storage Systems

2014

The ever-growing amount of data requires highly scalable storage solutions. The most flexible approach is to use storage pools that can be expanded and scaled down by adding or removing storage devices. To make this approach usable, it is necessary to provide a solution to locate data items in such a dynamic environment. This article presents and evaluates the Random Slicing strategy, which incorporates lessons learned from table-based, rule-based, and pseudo-randomized hashing strategies and is able to provide a simple and efficient strategy that scales up to handle exascale data. Random Slicing keeps a small table with information about previous storage system insert and remove operations…

DesignComputer scienceDistributed computingPerformancestorage managementHash function0102 computer and information sciences02 engineering and technologyParallel computingUSable01 natural sciencesSlicingrandomized data distributionAffordable and Clean Energy0202 electrical engineering electronic engineering information engineeringRandomnessExperimentationscalabilityPseudorandom number generatorbusiness.industry020206 networking & telecommunicationsReliabilityData FormatPRNG010201 computation theory & mathematicsHardware and ArchitectureComputer data storageScalabilityTable (database)businessNetworking & Telecommunications
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On a Non-periodic Shrinking Generator

2011

We present a new non-periodic random number generator based on the shrinking generator. The A-sequence is still generated using a LFSR, but the S-sequence is replaced by a finitely generated bi-ideal - a non-periodic sequence. The resulting pseudo-random sequence performs well in statistical tests. We show a method for the construction of an infinite number of finitely generated bi-ideals from a given A-sequence, such that the resulting sequence of the shrinking generator is nonperiodic. Further we prove the existence of what we call universal finitely generated bi-ideals that produce non-periodic words when used as the S-sequence of a shrinking generator for all non-trivial periodic A-sequ…

Discrete mathematicsPseudorandom number generatorSequenceRandom number generationSelf-shrinking generatorAutomata theoryTopologyElectronic mailStatistical hypothesis testingMathematicsShrinking generator2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
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Random Walk in a N-cube Without Hamiltonian Cycle to Chaotic Pseudorandom Number Generation: Theoretical and Practical Considerations

2017

Designing a pseudorandom number generator (PRNG) is a difficult and complex task. Many recent works have considered chaotic functions as the basis of built PRNGs: the quality of the output would indeed be an obvious consequence of some chaos properties. However, there is no direct reasoning that goes from chaotic functions to uniform distribution of the output. Moreover, embedding such kind of functions into a PRNG does not necessarily allow to get a chaotic output, which could be required for simulating some chaotic behaviors. In a previous work, some of the authors have proposed the idea of walking into a $\mathsf{N}$-cube where a balanced Hamiltonian cycle has been removed as the basis o…

FOS: Computer and information sciencesUniform distribution (continuous)Computer Science - Cryptography and SecurityComputer scienceHamiltonian CycleChaoticPseudorandom Numbers GeneratorFOS: Physical sciences02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]01 natural sciencesUpper and lower bounds[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computingsymbols.namesake[INFO.INFO-MC]Computer Science [cs]/Mobile Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]0202 electrical engineering electronic engineering information engineeringApplied mathematics[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]0101 mathematicsEngineering (miscellaneous)Pseudorandom number generatorChaotic IterationsBasis (linear algebra)Applied Mathematics020208 electrical & electronic engineering010102 general mathematicsRandom walkNonlinear Sciences - Chaotic DynamicsHamiltonian path[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationNonlinear Sciences::Chaotic Dynamics[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Modeling and SimulationRandom Walk[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]symbolsPseudo random number generator[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Chaotic Dynamics (nlin.CD)[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Cryptography and Security (cs.CR)
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On the collision property of chaotic iterations based post-treatments over cryptographic pseudorandom number generators

2018

International audience; There is not a proper mathematical definition of chaos, we have instead a quite big amount of definitions, each of one describes chaos in a more or less general context. Taking in account this, it is clear why it is hard to design an algorithm that produce random numbers, a kind of algorithm that could have plenty of concrete appliceautifat (anul)d bions. However we must use a finite state machine (e.g. a laptop) to produce such a sequence of random numbers, thus it is convenient, for obvious reasons, to redefine those aimed sequences as pseudorandom; also problems arise with floating point arithmetic if one wants to recover some real chaotic property (i.e. propertie…

Pseudorandom number generator020203 distributed computingSequenceFinite-state machineDynamical systems theoryComputer science010102 general mathematicsChaotic[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]02 engineering and technology[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation01 natural sciences[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR][INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]0202 electrical engineering electronic engineering information engineering[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]0101 mathematicsBoolean functionAlgorithmRandomnessGenerator (mathematics)2018 IEEE Middle East and North Africa Communications Conference (MENACOMM)
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Domain Generation Algorithm Detection Using Machine Learning Methods

2018

A botnet is a network of private computers infected with malicious software and controlled as a group without the knowledge of the owners. Botnets are used by cybercriminals for various malicious activities, such as stealing sensitive data, sending spam, launching Distributed Denial of Service (DDoS) attacks, etc. A Command and Control (C&C) server sends commands to the compromised hosts to execute those malicious activities. In order to avoid detection, recent botnets such as Conficker, Zeus, and Cryptolocker apply a technique called Domain-Fluxing or Domain Name Generation Algorithms (DGA), in which the infected bot periodically generates and tries to resolve a large number of pseudorando…

Pseudorandom number generatorDomain generation algorithmAlphanumericComputer sciencebusiness.industryDomain Name SystemComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSBotnetDenial-of-service attackMachine learningcomputer.software_genreComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMSCryptoLockerMalwareArtificial intelligencebusinesscomputer
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Backoff Hardware Architecture for Inter-FPGA Traffic Management

2017

International audience; Multi-FPGA platforms are considered to be the mostappropriate experimental way to emulate a large Multi-ProcessorSystem-on-Chip based on a Network-on-Chip. However, theuse of a Network-on-Chip in several FPGAs requires inter-FPGA communication links to replace intra-FPGA links betweenrouters. As the ratio of the logic capacity to the number of IOsonly increases slowly with each generation of FPGA, IOs inFPGA are becoming a scare resource. And as there are morerouters than IOs, using a Network-on-Chip requires sharinginter-FPGA links between routers, and sharing an external linkcan lead to bottlenecks. Here, we evaluate the inter-FPGA trafficmanagement using a backoff…

Pseudorandom number generatorHardware architecturebusiness.industryComputer science020206 networking & telecommunications02 engineering and technology020202 computer hardware & architecture[INFO.INFO-ES] Computer Science [cs]/Embedded SystemsResource (project management)Network on a chipPRNGEmbedded system0202 electrical engineering electronic engineering information engineeringHardware_INTEGRATEDCIRCUITS[INFO.INFO-ES]Computer Science [cs]/Embedded Systems[ INFO.INFO-ES ] Computer Science [cs]/Embedded SystemsRouting (electronic design automation)ArchitecturebusinessField-programmable gate arrayinter-FPGA linkBackOff architectureNoC
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Compact and Field Portable Biophotonic Sensors for Automated Cell Identification (Plenary Address)

2021

In this Plenary address paper, we overview recently published work for automated cell identification using 3D optical imaging in compact and field portable biophotonic sensors. Digital holographic microscopy systems and lensless pseudorandom phase encoding systems capture 3D information of biological cells and make highly accurate automated cell identification possible. Overviewed systems include sickle cell disease diagnosis based on spatio-temporal cell dynamics in a field-portable 3D-printed shearing digital holography as well as lensless cell identification of both single and multicell samples using pseudorandom phase encoding.

Pseudorandom number generatorIdentification (information)Optical imagingbusiness.industryComputer scienceEncoding (memory)Digital holographic microscopybusinessComputer hardwareField (computer science)Digital holography
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Multidimensional pseudo-random pulse signals and their coincidence properties

1996

Pseudorandom number generatorPhysicsNuclear and High Energy PhysicsInstrumentationCoincidenceComputational physicsPulse (physics)Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
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