Secure random number generation in wireless sensor networks

Re, Giuseppe Lo; Milazzo, Fabrizio; Ortolani, Marco

doi:10.1145/2070425.2070453

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…We mean by RBSs group, the group of RBSs that have the same length and are generated from ECG signals acquired from the same dataset for a specific duration. Equation (12) gives the minimum number of tests that must be passed for each NIST test [31]:…”

Section: C) Randomness Evaluation Based On Nist Testsmentioning

confidence: 99%

“…Hence, they should be obtained from the output of an RNG [9]. In general, wireless sensor networks (WSNs) use PRNGs for generating random binary sequences (RBSs) [11], [12]. However, PRNGs require heavy computations to obtain randomness, and their seeds must be carefully chosen and protected, which in turn leads to the consumption of resources [11].…”

Section: Introductionmentioning

confidence: 99%

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A High-Throughput Random Binary Sequence Generator Based on ECG

Zenieh,

Al-Mahairi,

Haddad

2022

IEEE Access

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A Wireless Body Area Network (WBAN) is a network that expands over the human body, which consists of multiple nodes that are connected through wireless channels. It offers many applications in the area of remote health care. Maintaining the security of health information in WBAN is an essential requirement. One aspect of ensuring WBAN security is the generation of random binary sequences (RBSs), e.g., encryption keys generation. Due to the very limited resources of WBAN sensors, traditional pseudorandom number generators cannot be used. To reduce resource consumption, some researchers suggested using biometrics in generating RBSs, specifically the electrocardiogram (ECG) signal. However, their methods suffer from low throughput, so they are not suitable for real-time healthcare applications. In this paper, we present a new random sequence generator based on the ECG signal. Our contribution is to build a random sequence generator that generates different length RBSs and has throughput tens or hundreds of times higher than previous methods. Our generator reduces resource consumption due to its very simple processing operations. To evaluate the proposed generator, RBSs of different lengths (128, 256, 512, 1024, 2048 bits) were generated from two ECG datasets, the first is for healthy people, and the second is for people who suffer from arrhythmia. The randomness and distinctiveness of the generated RBSs are measured using the National Institute of Standards and Technology (NIST) statistical tests and the Hamming distance. Thus, we have proved that the resulting RBSs are appropriate for information security applications.

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Section: C) Randomness Evaluation Based On Nist Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A High-Throughput Random Binary Sequence Generator Based on ECG

Zenieh,

Al-Mahairi,

Haddad

2022

IEEE Access

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“…У праці [8] автори розробили та впровадили СГВЧ, криптографічний генератор псевдовипадкових чисел, який використовує отримані бітові помилки як джерело випадковості у вузлах бездротових датчиків. У праці [9] автори представили вдосконалену версію СГВЧ, запропоновану в праці [10], яка використовує вимірювання, отримані від бездротових вузлів датчиків, як джерела фізичної випадковості. Їх метод використовує розподілений алгоритм виборів лідерів для вибору випадкового джерела даних.…”

Section: постановка проблемиunclassified

Модель Легковагового Генератора Псевдовипадкових Чисел Для Інтернету Речей

Popereshnyak¹,

Райчев

2021

SBT

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Стаття присвячена розробці легких генераторів псевдовипадкових чисел. Описано принципи проектування легковагового генератора псевдовипадкових чисел. Сформульовано вимоги до якості легковагового генератора псевдовипадкових чисел. Було розглянуто побудову фізичної моделі легковагового генератора псевдовипадкових чисел.

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“…In order to improve the original TRNG module, our recent proposal suggested that the task of providing source data for the random number generator was shared among multiple nodes, so as to hide the actual source of data from potential attackers. The random number generation process was made to depend on multiple sources via an authenticated readings collection (ARC) protocol, so that the attacker would be forced to tamper with a higher number of sensor boards before being able to gather a sufficient amount of information and break the TRNG.…”

Section: A Trng For Wireless Sensor Nodesmentioning

confidence: 99%

Secure random number generation in wireless sensor networks

Milazzo

Ortolani

2014

Concurrency and Computation

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The increasing adoption of wireless sensor networks as a flexible and inexpensive tool for the most diverse applications, ranging from environmental monitoring to home automation, has raised more and more attention to the issues related to the design of specifically customized security mechanisms. The scarcity of computational, storage, and bandwidth resources cannot definitely be disregarded in such context, and this makes the implementation of security algorithms particularly challenging. This paper proposes a security framework for the generation of true random numbers, which are paramount as the core building block for many security algorithms; the intrinsic nature of wireless sensor nodes and their capability of reliably providing measurements of environmental quantities make them natural candidates as true random number generators. In order to provide robustness to common attacks, we additionally devised a protocol aimed at obscuring the actual source of data, by making nodes cooperate with their neighbors. Furthermore, we describe an enhanced version of our framework consisting in an optimization for use in the context of resource-constrained systems. SECURE RANDOM NUMBER GENERATION IN WIRELESS SENSOR NETWORKS 3843 the produced sequences show some periodicity and are actually reproducible. The attacker could in principle collect generated random numbers until the sequence restarts and then deterministically predict the rest of the sequence; a common used countermeasure consists in periodically changing the key after a certain time interval. PRNGs are widely used because they ensure good statistical properties and high bit rates; however, their viability completely depends on the actual randomness of the initialization seeds.As the name suggests, the physical approach relies on measurements related to physical phenomena; in this case, random numbers are generated as a function of a set of samples coming from sensory readings, and the generator is named a true random number generator (TRNG), because the sequence is actually non-deterministic and unpredictable. TRNGs do not need seeds nor secret keys and do not exhibit periodicity because of the independence of the current random number from its past values. TRNGs usually require post-processing operations because they often do not ensure sufficiently good statistical properties, so they are intrinsically characterized by lower bit rates with respect to the PRNGs counterpart. TRNGs are thus often used as random seed generators for PRNGs because they are too slow for standalone usage in applications requiring high-bit-rate random sequences.However, secure applications in the specific context of WSNs seldom require high-bit-rate random sequences; we will thus focus on TRNGs as the basic building block for a security infrastructure for WSNs.Moreover, sensor nodes are obvious candidates as data providers for TRNGs, because their natural purpose is to collect great amount of data for environmental monitoring. As reported in the recent literature, many of the typic...

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Secure random number generation in wireless sensor networks

Cited by 13 publications

References 17 publications

A High-Throughput Random Binary Sequence Generator Based on ECG

A High-Throughput Random Binary Sequence Generator Based on ECG

Модель Легковагового Генератора Псевдовипадкових Чисел Для Інтернету Речей

Secure random number generation in wireless sensor networks

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