一个自我振荡的有机突触用于内存双因素身份验证
Shuzhi Liu1,2, Xiaolong Zhong1, Yuxuan Li2
1Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|March 23, 2024
概括
本研究介绍了用于AI目标识别的内存双因素身份验证 (IM-2FA). 它通过将不对称加密与卷积神经网络 (ConvNets) 集成,有效地防止未经授权的访问.
科学领域:
- 神经形态计算是一种神经形态计算.
- 人工智能 (AI) 是一种人工智能.
- 网络安全 网络安全
背景情况:
- 人工智能2.0时代需要强大的生物灵感目标识别.
- 目标识别系统面临劫持和未经授权访问的风险.
- 在神经形态计算之前,预验证加密至关重要.
研究的目的:
- 开发一种内存不对称加密方法,用于目标识别中的预验证.
- 实施内存双因素身份验证 (IM-2FA) 战略.
- 增强用于目标识别的卷积神经网络 (ConvNets) 的安全性.
主要方法:
- 利用时间变化的突触规则来进行内存不对称的加密.
- 采用单极自我振荡的突触行为进行加密,减少了外围电路的复杂性.
- 集成加密与随后的卷积神经网络 (ConvNet) 进行目标识别.
主要成果:
- 没有适当的加密,ConvNet对目标识别的准确性下降到0.86%以下.
- 在适当的时间用正确的权重成功加密,使得识别率达到99.82%.
- 证明有效地阻止非自愿访问和验证IM-2FA战略.
结论:
- 拟议的IM-2FA战略显著提高了基于AI的目标识别的安全性.
- 内存不对称加密有效地防止在ConvNet处理之前未经授权的访问.
- 该系统为神经形态计算应用中的预验证提供了安全有效的解决方案.
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