How some market random number generators are now known to be weak
Pseudorandom Number Generators are deterministic algorithms which take in a value obtained from an entropy source, a seed, as input and output a sequence of values that appears to be generated independently with no distinguishable patterns. This project will evaluate three different known pseudorand...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/137832 |
| _version_ | 1824455318484025344 |
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| author | Zhou, Nan |
| author2 | Tay Kian Boon |
| author_facet | Tay Kian Boon Zhou, Nan |
| author_sort | Zhou, Nan |
| collection | NTU |
| description | Pseudorandom Number Generators are deterministic algorithms which take in a value obtained from an entropy source, a seed, as input and output a sequence of values that appears to be generated independently with no distinguishable patterns. This project will evaluate three different known pseudorandom number generator algorithms used and investigate its possible vulnerabilities. The goal of this project is to understand the vulnerabilities of these pseudorandom number generators and thus reduce the risk of damage from potential attacks arising from exploitation of these vulnerabilities.
First, the criteria of a cryptographically secure random number generator are investigated through literature review. Next, three different type of well-known Pseudorandom Number Generators: Linear Congruential Generator, Dual Elliptic Curve Deterministic Random Bit Generator and Mersenne Twister are investigated.
Overall, it is important to ensure that the entropy source for the seed is sufficiently random and take into consideration the level of forward and backwards secrecy of the deterministic algorithm. |
| first_indexed | 2025-02-19T03:36:18Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/137832 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:36:18Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1378322020-04-16T01:05:11Z How some market random number generators are now known to be weak Zhou, Nan Tay Kian Boon School of Computer Science and Engineering kianboon.tay@ntu.edu.sg Engineering::Computer science and engineering Pseudorandom Number Generators are deterministic algorithms which take in a value obtained from an entropy source, a seed, as input and output a sequence of values that appears to be generated independently with no distinguishable patterns. This project will evaluate three different known pseudorandom number generator algorithms used and investigate its possible vulnerabilities. The goal of this project is to understand the vulnerabilities of these pseudorandom number generators and thus reduce the risk of damage from potential attacks arising from exploitation of these vulnerabilities. First, the criteria of a cryptographically secure random number generator are investigated through literature review. Next, three different type of well-known Pseudorandom Number Generators: Linear Congruential Generator, Dual Elliptic Curve Deterministic Random Bit Generator and Mersenne Twister are investigated. Overall, it is important to ensure that the entropy source for the seed is sufficiently random and take into consideration the level of forward and backwards secrecy of the deterministic algorithm. Bachelor of Engineering (Computer Science) 2020-04-16T01:05:10Z 2020-04-16T01:05:10Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/137832 en SCSE19-0151 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Zhou, Nan How some market random number generators are now known to be weak |
| title | How some market random number generators are now known to be weak |
| title_full | How some market random number generators are now known to be weak |
| title_fullStr | How some market random number generators are now known to be weak |
| title_full_unstemmed | How some market random number generators are now known to be weak |
| title_short | How some market random number generators are now known to be weak |
| title_sort | how some market random number generators are now known to be weak |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/137832 |
| work_keys_str_mv | AT zhounan howsomemarketrandomnumbergeneratorsarenowknowntobeweak |