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相关概念视频

Long-term Potentiation01:35

Long-term Potentiation

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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A Method for Growing Bio-memristors from Slime Mold
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一个强大的深度学习攻击免疫基于MRAM的物理非克隆功能.

Mohammad Javad Adel1, Mohammad Hadi Rezayati1, Mohammad Hossein Moaiyeri2

  • 1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, 1983969411, Iran.

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|September 4, 2024
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概括

这项研究引入了一种新的硬件安全电路,用于物理非可克隆函数 (PUF) 的磁还原性随机访问存储器 (MRAM). 基于MRAM的PUF对机器学习攻击有很强的抵抗力,确保安全的电子设备信息.

关键词:
基于深度学习 (DL) 的模拟攻击.新兴技术 新兴技术硬件安全原始的原始化.基于机器学习 (ML) 的模拟攻击.磁道连接点 (MTJ) 是一个磁道连接点.物理非克隆功能 (PUF)

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 材料科学 材料科学 材料科学

背景情况:

  • 电子设备需要先进的硬件安全性来保护敏感数据.
  • 物理非克隆功能 (PUF) 通过利用独特的设备变体,为硬件安全提供了一个有前途的解决方案.
  • 现有的PUF设计在满足严格的安全标准和抵御复杂的攻击方面面临挑战.

研究的目的:

  • 提出和评估一个基于磁电复原随机存取存储器 (MRAM) 的新型物理不可克隆功能 (PUF) 电路.
  • 评估基于MRAM的PUF对各种机器学习 (ML) 和深度学习 (DL) 建模攻击的安全性和性能.
  • 分析电路在布局层面的效率及其遵守安全标准,如严格的雪崩标准 (SAC).

主要方法:

  • 设计了一个PUF电路,利用MRAM中磁道结 (MTJ) 细胞的固有制造变化.
  • 模拟机器学习攻击,包括多层感知器 (MLP),线性回归 (LR) 和支持矢量机器 (SVM),在两阵列和四阵列架构上.
  • 采用深度学习模型,如卷积神经网络 (CNN) 和循环神经网络 (RNN) 来进行高维攻击模拟.
  • 评估了电路性能分析的内和间的哈明距离 (HD) 和扩散度.

主要成果:

  • 基于MRAM的PUF有效地满足严格的雪崩标准 (SAC),表现优于传统的仲裁PUF.
  • 机器学习攻击显示出较低的预测准确性 (例如,MLP为两个数组的53.61%,为四个数组的49.87%),表明了强度.
  • 深度学习攻击也产生了较低的准确性 (约为50.31%),证实了对高级建模的阻力.
  • 该电路表现出有利的哈明内部距离 (0.98%) 和扩散性 (49.09%),可接受的哈明间距离 (49.96%).

结论:

  • 拟议的基于MRAM的PUF电路为电子设备提供了强大而安全的硬件安全解决方案.
  • 它的网格式结构和依赖MTJ阻力变异提供了对ML/DL建模攻击的强有力的防御.
  • 该电路满足关键性能指标,使其成为下一代硬件安全应用程序的可行选择.