Security Flaws in an Efficient Pseudo-Random Number Generator for Low-Power Environments

Peris-Lopez, Pedro; Hernandez-Castro, Julio C.; Tapiador, Juan E.; Millan, Enrique San; Lubbe, Jan C. A. van der

doi:10.1007/978-3-642-11526-4_3

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…They use an 8‐bit clock as random seed generator and the 8‐bit CRC of the received packets to implement rekeying. In principle, the method could be resilient to combined attacks as the randomness comes from different physical sources; however, the work in showed that such generator suffers of many design flaws. First of all, under clock tampering attack, the sequence restarts each three time steps; that is, RN ( t ) = RN ( t + 3 * m ) for any m ∈ Z and the generator becomes completely predictable; the authors propose to rekey the method every 10 time steps, but a blackhole attack and radio tampering force the generator to remain within its three values forever.…”

Section: Comparison Against Other Trngsmentioning

confidence: 99%

Secure random number generation in wireless sensor networks

Milazzo

Ortolani

2014

Concurrency Computat.: Pract. Exper.

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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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Section: Comparison Against Other Trngsmentioning

confidence: 99%

Secure random number generation in wireless sensor networks

Milazzo

Ortolani

2014

Concurrency Computat.: Pract. Exper.

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“…In a related work [14], an 8-bit PRNG (i-Beam sensor) has initially been proposed that xored the current value of the time with a key, reaching a throughput of 400Mbps. However, this PRNG has revealed to have a short period, as described in [15]. TinyRNG [16] is another PRNG that combines two cipher blocks based on chaining message authentication code (CBC-MAC) and on counter mode (CTR), where the first one aims to extract the transmission of bit errors on the network as randomness sources to reseed the second cipher key.…”

Section: Introductionmentioning

confidence: 99%

“…,109, 120, 107, 126, 125, 112, 103, 114, 116, 118, 123, 98, 121, 96, 79, 78, 111, 124, 75, 122, 97, 108, 71, 100, 117, 101, 115, 66, 113,64, 127, 90, 89, 94, 83, 91, 81, 92, 95, 84, 87, 85, 82, 86, 80, 88, 77, 76, 93, 72, 74, 106, 105, 104, 69, 102, 68, 70, 99, 67, 73,65,55,58,57,44, 187, 186,49,60, 119,52,37,53,35,54, 177,56,45,62,61,40,59,10,9, 168, 167, 166,36,38, 163, 162,41,48,23,28,13,24, 155,30,29,16,21, 150,20,22,27,19, 145,17, 143, 142,15,14,43,11,1,12,39,4,7, 132,2,34, 0, 128] F2[223, 190, 249, 254, 243, 186, 233, 252, 183, 182, 247, 228, 242, 226,...…”

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confidence: 99%

A Hardware and Secure Pseudorandom Generator for Constrained Devices

Bakiri¹,

Guyeux

Couchot

et al. 2018

IEEE Trans. Ind. Inf.

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Hardware security for an Internet of Things (IoT) 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 resourceconstrained 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 effects of a slight modification of the seed (proven to be sensitive to the initial conditions) or of the inputted generator cannot be predicted. The proposal has been fully deployed on a FPGA and 65nm ASIC, it runs completely in parallel while consuming as low resources as possible, and achieving: (a) 11.5 Gbps for FPGA and 9.4 Gbps for ASIC random bit throughput, (b) 3.3µW (LF) to 7.8mW (UHF) total power consumption with 5% leakage power, measured at 1.32V , and (c) able to successfully pass the statistical tests of NIST and TestU01 (BigCrush).

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Security Flaws in an Efficient Pseudo-Random Number Generator for Low-Power Environments

Cited by 3 publications

References 3 publications

Secure random number generation in wireless sensor networks

Secure random number generation in wireless sensor networks

A Hardware and Secure Pseudorandom Generator for Constrained Devices

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