利用机器学习对硬件椭圆曲线标量乘法进行非定位无监督水平迭代攻击

Future Internet Pub Date : 2024-01-29 DOI:10.3390/fi16020045
Marcin Aftowicz, I. Kabin, Z. Dyka, P. Langendörfer
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引用次数: 0

摘要

物联网技术在让工业、城市和家庭变得更加智能的同时,也为安全风险敞开了大门。只要有合适的设备和对设备的物理访问权限,攻击者就能利用侧信道信息(如时序、功耗或电磁辐射)破坏加密操作并提取秘钥。本研究介绍了对椭圆曲线标量乘法运算加密硬件加速器的侧信道分析,该加速器是在现场可编程门阵列和特定应用集成电路中实现的。所提出的框架包括使用最先进的统计水平攻击进行初始密钥提取,然后使用正则化人工神经网络,将水平攻击中部分错误的密钥猜测作为输入,并对其进行迭代修正。通过迭代学习,水平攻击的初始正确率(以正确提取密钥比特的分数来衡量)从 75% 提高到 98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-Profiled Unsupervised Horizontal Iterative Attack against Hardware Elliptic Curve Scalar Multiplication Using Machine Learning
While IoT technology makes industries, cities, and homes smarter, it also opens the door to security risks. With the right equipment and physical access to the devices, the attacker can leverage side-channel information, like timing, power consumption, or electromagnetic emanation, to compromise cryptographic operations and extract the secret key. This work presents a side channel analysis of a cryptographic hardware accelerator for the Elliptic Curve Scalar Multiplication operation, implemented in a Field-Programmable Gate Array and as an Application-Specific Integrated Circuit. The presented framework consists of initial key extraction using a state-of-the-art statistical horizontal attack and is followed by regularized Artificial Neural Networks, which take, as input, the partially incorrect key guesses from the horizontal attack and correct them iteratively. The initial correctness of the horizontal attack, measured as the fraction of correctly extracted bits of the secret key, was improved from 75% to 98% by applying the iterative learning.
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