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相关概念视频

Semiconductors01:22

Semiconductors

There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
Seizures l: Introduction01:20

Seizures l: Introduction

Understanding seizures and epilepsy relies on key definitions that help in recognizing, classifying, and managing these disorders. These definitions provide a framework for recognizing, classifying, and managing seizure disorders.DefinitionsA seizure is a sudden, abnormal burst of electrical activity in the brain that can cause changes in awareness, movement, sensation, or behavior, depending on the area involved. Epilepsy is a chronic condition characterized by recurrent, unprovoked seizures,...

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基于 DSE 的硬件木马攻击神经网络加速器在 FPGA 上.

Chao Guo, Masao Yanagisawa, Youhua Shi

    IEEE transactions on neural networks and learning systems
    |October 28, 2024
    PubMed
    概括

    本研究介绍了一个自动化框架,用于在设计空间探索 (DSE) 中生成深度神经网络 (DNN) 的硬件木马 (HT). 这种新的方法将隐形HT嵌入加速器IP中,使控制攻击的开销最小.

    科学领域:

    • 计算机工程 计算机工程
    • 网络安全 网络安全
    • 人工智能的人工智能

    背景情况:

    • 设计空间探索 (DSE) 通过自动生成最佳配置和加速器知识属性 (IP) 来加速深度神经网络 (DNN) 的部署.
    • 尽管DSE工具的广泛使用,但DSE的安全影响,特别是关于硬件木马 (HTs) 的安全影响,在很大程度上被忽视了.
    • 现有的DSE方法专注于优化,在自动化IP生成过程中嵌入对抗性攻击的漏洞.

    研究的目的:

    • 从对抗的角度探索设计空间探索 (DSE) 的安全漏洞.
    • 提出和评估一个自动化框架,用于生成嵌入 DSE.内硬件木马 (HTs).
    • 为了证明隐形HTs在导致DNN的可控精度降低和特定类别攻击方面的有效性.

    主要方法:

    • 开发了一个自动化的硬件木马 (HT) 生产框架,集成到 DSE 过程中.
    • 利用进化算法 (EA) 分析用户输入数据,自动生成攻击代码.
    • 将生成的HT嵌入到DNN的最终加速器知识产权 (IP) 设计中.

    主要成果:

    • 拟议的HT框架成功地将隐形木马嵌入到FPGAs的加速器IP中,适用于单个和多领域设计.
    • 在LeNet,VGG-16和YOLO模型上的实验表明了显著的精度下降和成功的特定类别攻击,例如,LeNet的错误分类率为97.3%.

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  • 与无妥协设计相比,HT设计的开销最小,LUT使用率不超过0.6%.

    • 结论:

    • 与DSE集成的自动化HT生成框架对DNN部署构成重大安全威胁.
    • 拟议的HT的隐形性质和可控攻击能力凸显了对DSE加强安全措施的需要.
    • 需要进行进一步的研究,以开发针对自动化硬件设计流中的这种嵌入式对抗性攻击的强大防御.