基于时频特征融合的随机数源的在线评估方法
Qian Sun1,2, Kainan Ma1, Yiheng Zhou1
1Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
评估高安全性应用程序的源是具有挑战性的. 这项研究表明,最小与预测准确性相关,并且新的深度学习模型FFT-ATT-LSTM提供了高效的在线质量评估.
科学领域:
- 密码学和信息安全信息安全
- 机器学习和人工智能的人工智能
- 硬件安全 硬件安全
背景情况:
- 传统的源评估方法,通常是统计的,缺乏在芯片上或在线部署的能力.
- 在线评估源质量对于高级加密应用程序至关重要.
- 现有的方法很难提供实时反随机性质.
研究的目的:
- 建立一种新的,可在线部署的方法来评估源质量.
- 为了研究最小和随机序列的预测准确性之间的相关性.
- 为实时源评估开发一个高效的深度学习模型.
主要方法:
- 使用皮尔森相关系数将最小值与预测准确度相关联的实验分析.
- 开发和应用一种新的深度学习架构:快速里埃转换-注意力机制-长期短期记忆网络 (FFT-ATT-LSTM).
- 快里叶变换 (FFT) 与功能融合的简化软注意力机制的集成.
主要成果:
- 在最小和预测准确性之间发现了显著的负相关性 (r = -0.925,p < 1.07 × 10^-7).
- 拟议的FFT-ATT-LSTM模型实现了比基线网络预测精度提高4.46%和8%.
- FFT-ATT-LSTM展示了一个紧的参数大小 (33.90 KB),在效率方面表现优于TCN和变压器.
结论:
- 最小作为预测随机序列质量的可靠指标.
- FFT-ATT-LSTM模型为在线源评估提供了准确且资源高效的解决方案.
- 开发的模型在需要实时随机性评估的安全系统中具有重要的实际应用潜力.
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