一个基于对比学习的硬件木马检测框架
1Electronic Engineering College, Heilongjiang University, Harbin, 150080, China.
Scientific reports
|December 27, 2024
概括
这项研究引入了一种用于使用对比学习和功耗数据检测硬件木马 (HT) 的新框架. 该方法提高了检测效率,特别是在未经监督的环境中对未知的威胁.
科学领域:
- 硬件安全 硬件安全
- 集成电路设计 集成电路设计
- 计算机工程是计算机工程.
背景情况:
- 硬件木马 (HT) 在设计,制造和部署方面对半导体行业构成重大威胁.
- 侧通道分析,特别是使用功耗,是检测HT的关键非接触方法,因为它的效率和准确性.
研究的目的:
- 通过使用对比学习,提出一种用于硬件木马检测的新框架.
- 为了应对未经监督或监督较弱的检测场景的挑战.
- 提高HT检测模型的概括能力.
主要方法:
- 一个基于使用电力消耗信息进行对比学习的HT检测框架.
- 数据增强技术,包括一维离散混乱映射,以增强模型概括性.
- 通过比较样本的相似性和差异来学习模型表示,减少对标记数据的依赖.
- 使用骨干网络对侧通道信息进行分类,以有效检测HT.
主要成果:
- 拟议的对比学习框架显示了HT检测的优越泛化能力.
- 在较小的木马数据集上训练的模型在较大的木马上显示了显著的检测优势 (高达44%).
- 在较大的木马数据集上训练的模型也在较小的木马上显示了优势 (高达10%).
- 该框架在不平衡和杂的数据环境中有效运行.
结论:
- 对比式学习框架在未经监督或监督较弱的场景中检测未知的硬件木马非常有效.
- 与传统方法相比,该方法提供了更好的检测效率和概括性.
- 这种方法通过提供强大的解决方案来识别恶意植入物来提高硬件安全性.
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