一种基于深度学习的硬件木马检测成本驱动的方法
Chen Dong1, Yinan Yao1, Yi Xu1
1College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了MHTtext,这是一种用于检测集成电路中的硬件木马的深度学习模型. 它提供灵活的策略来平衡精度和计算成本,提高网络物理系统和Metaverse的安全性.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络安全 网络安全
背景情况:
- 网络物理系统和Metaverse面临越来越多的硬件安全威胁,特别是集成电路中的硬件木马.
- 现有的硬件木马检测方法因金色芯片和高计算需求等局限性而难以进行大规模集成.
- 传统的机器学习方法用于硬件木马检测往往是不稳定的,因为手动特征提取的困难.
研究的目的:
- 提出一种新的基于深度学习的多尺度检测模型,MHTtext,用于自动硬件木马特征提取和识别.
- 在MHTtext中开发策略,以平衡实际应用的检测准确性和计算效率.
- 引入一个新的评估指标,稳定效率指数 (SEI),用于评估模型的性能和稳定性.
主要方法:
- MHTtext模型使用深度学习从网列数据中自动提取特征.
- 实施了两种不同的策略 (全球和本地),以满足不同的准确性和计算要求.
- 文本CNN用于硬件特洛伊木马识别,具有确保不重复组件信息的机制,以提高稳定性.
主要成果:
- 在MHTtext的全球策略中,在基准网清单上检测硬件木马的平均准确率达到99.26%.
- MHTtext模型表现出高度的稳定性和灵活性,其中一个策略在SEI中在比较分类器中排名第一.
- 当地战略也取得了出色的结果,证明了该模型在不同操作模式中的有效性.
结论:
- 拟议的MHTtext模型为大型集成电路中的硬件木马检测提供了一个稳定,灵活和准确的解决方案.
- 双策略方法允许基于特定应用需求的可适应性性能,解决传统方法的局限性.
- MHTtext有助于提高像Metaverse这样的不断发展的数字环境中的关键硬件组件的安全性.
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