2021
DOI: 10.3390/app11083330
|View full text |Cite
|
Sign up to set email alerts
|

True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA

Abstract: Random numbers are widely employed in cryptography and security applications. If the generation process is weak, the whole chain of security can be compromised: these weaknesses could be exploited by an attacker to retrieve the information, breaking even the most robust implementation of a cipher. Due to their intrinsic close relationship with analogue parameters of the circuit, True Random Number Generators are usually tailored on specific silicon technology and are not easily scalable on programmable hardwar… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
27
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
7
3

Relationship

2
8

Authors

Journals

citations
Cited by 36 publications
(40 citation statements)
references
References 35 publications
1
27
0
Order By: Relevance
“…Recently, Nannipieri et al [ 43 ] developed a true random number generator based on Fibonacci-Galois Ring Oscillators. They implemented the proposed methodology on FPGA.…”
Section: Chaotic Mapsmentioning
confidence: 99%
“…Recently, Nannipieri et al [ 43 ] developed a true random number generator based on Fibonacci-Galois Ring Oscillators. They implemented the proposed methodology on FPGA.…”
Section: Chaotic Mapsmentioning
confidence: 99%
“…The internal architecture of the RNG engine is illustrated in Figure 1 , and it mainly consists of the integration of an entropy source module and a hash-based DRBG module, which are respectively described in [ 6 , 7 , 8 ]. The former is responsible for the generation of (internal) seeds that are used to initialize the internal state of the DRBG and that are built byte by byte, exploiting the Buffer unit to collect them, while the latter produces sequences of random words that constitute the output data of the whole RNG engine.…”
Section: Design Of the Random Number Generatormentioning
confidence: 99%
“…To crack a key using a brute force attack, the attacker needs 2 k−1 (k=key length in bits). If the potential keys are not chosen randomly, entropy fails to capture the required number of guesses [46]. The entropy values are situated at an optimum interval, as shown in Table 8.…”
Section: Shannon Entropy Analysismentioning
confidence: 99%