Design and FPGA implementation of a novel cryptographic secure pseudo random number generator based on artificial neural networks and chaotic systems

IF 2.2 3区 工程技术 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Integration-The Vlsi Journal Pub Date : 2025-03-08 DOI:10.1016/j.vlsi.2025.102388
Youcef Alloun , Abdenour Kifouche , Mohamed Salah Azzaz , Mahdi Madani , El-Bay Bourennane , Said Sadoudi
{"title":"Design and FPGA implementation of a novel cryptographic secure pseudo random number generator based on artificial neural networks and chaotic systems","authors":"Youcef Alloun ,&nbsp;Abdenour Kifouche ,&nbsp;Mohamed Salah Azzaz ,&nbsp;Mahdi Madani ,&nbsp;El-Bay Bourennane ,&nbsp;Said Sadoudi","doi":"10.1016/j.vlsi.2025.102388","DOIUrl":null,"url":null,"abstract":"<div><div>A secure random number generator (RNG) is crucial for cryptography and data protection applications. Many existing approaches employ classical chaotic systems, which have been demonstrated as vulnerable to some attacks. Therefore, this research proposes the design on FPGA of a new pseudo-RNG based on an artificial neural network (ANN) and chaotic systems. Initially, a multi-layer perceptron (MLP) with a hardware friendly activation function (AF) is trained to mimic the behavior of the unified chaotic system (UCS). To mitigate chaos degradation and the difference between the training and the inference, the scheduled sampling technique is adapted and applied to the MLP network. Once the model is well-tuned, its chaotic nature is validated by calculating the Lyapunov exponents and determining the fractal dimension. The pre-trained model based on which an MLP-based Chaotic Pseudo-RNG (MLP-CPRNG) is then implemented on FPGA using VHDL language and Xilinx Vivado design suite. To improve the generator’s output capabilities, a technique named the <span><math><mi>d</mi></math></span>-lagged differencing (<span><math><mi>d</mi></math></span>-LD) is implemented as a part of the MLP-CPRNG. The implemented MLP-CPRNG outperforms the existing works in terms of resource utilization, which makes it suitable for resource-constrained environment. It also offers extended key space and has successfully passed performance tests such as NIST statistical tests, entropy measurement, and correlation analysis. These results highlight the robustness of MLP-CPRNG against brute-force, algebraic and statistical attacks, thus its suitability for embedded cryptographic applications.</div></div>","PeriodicalId":54973,"journal":{"name":"Integration-The Vlsi Journal","volume":"103 ","pages":"Article 102388"},"PeriodicalIF":2.2000,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integration-The Vlsi Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167926025000458","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0

Abstract

A secure random number generator (RNG) is crucial for cryptography and data protection applications. Many existing approaches employ classical chaotic systems, which have been demonstrated as vulnerable to some attacks. Therefore, this research proposes the design on FPGA of a new pseudo-RNG based on an artificial neural network (ANN) and chaotic systems. Initially, a multi-layer perceptron (MLP) with a hardware friendly activation function (AF) is trained to mimic the behavior of the unified chaotic system (UCS). To mitigate chaos degradation and the difference between the training and the inference, the scheduled sampling technique is adapted and applied to the MLP network. Once the model is well-tuned, its chaotic nature is validated by calculating the Lyapunov exponents and determining the fractal dimension. The pre-trained model based on which an MLP-based Chaotic Pseudo-RNG (MLP-CPRNG) is then implemented on FPGA using VHDL language and Xilinx Vivado design suite. To improve the generator’s output capabilities, a technique named the d-lagged differencing (d-LD) is implemented as a part of the MLP-CPRNG. The implemented MLP-CPRNG outperforms the existing works in terms of resource utilization, which makes it suitable for resource-constrained environment. It also offers extended key space and has successfully passed performance tests such as NIST statistical tests, entropy measurement, and correlation analysis. These results highlight the robustness of MLP-CPRNG against brute-force, algebraic and statistical attacks, thus its suitability for embedded cryptographic applications.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
Integration-The Vlsi Journal
Integration-The Vlsi Journal 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.30%
发文量
107
审稿时长
6 months
期刊介绍: Integration''s aim is to cover every aspect of the VLSI area, with an emphasis on cross-fertilization between various fields of science, and the design, verification, test and applications of integrated circuits and systems, as well as closely related topics in process and device technologies. Individual issues will feature peer-reviewed tutorials and articles as well as reviews of recent publications. The intended coverage of the journal can be assessed by examining the following (non-exclusive) list of topics: Specification methods and languages; Analog/Digital Integrated Circuits and Systems; VLSI architectures; Algorithms, methods and tools for modeling, simulation, synthesis and verification of integrated circuits and systems of any complexity; Embedded systems; High-level synthesis for VLSI systems; Logic synthesis and finite automata; Testing, design-for-test and test generation algorithms; Physical design; Formal verification; Algorithms implemented in VLSI systems; Systems engineering; Heterogeneous systems.
期刊最新文献
Design and FPGA implementation of a novel cryptographic secure pseudo random number generator based on artificial neural networks and chaotic systems Resource-efficient and ultra-high throughput LDPC decoder for CCSDS near-earth standard Rich dynamics and analog implementation of a Hopfield neural network in integer and fractional order domains Design of a low-power, low-PDP dual modulus CML frequency divider for ZigBee application Design and practical implementation of a novel hyperchaotic system generator based on Apéry's constant
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1