找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Smart Card Research and Advanced Applications; 17th International C Begül Bilgin,Jean-Bernard Fischer Conference proceedings 2019 Springer

[复制链接]
楼主: Jaundice
发表于 2025-3-28 15:54:33 | 显示全部楼层
发表于 2025-3-28 19:15:52 | 显示全部楼层
Yet Another Size Record for AES: A First-Order SCA Secure AES S-Box Based on , Multiplication, multiplication chain of length 4. Based on this representation, we showcase a very compact S-box circuit with only one .-multiplier instance. Thereby, we introduce a new high-level representation of the AES S-box and set a new record for the smallest first-order secure implementation.
发表于 2025-3-29 00:52:50 | 显示全部楼层
发表于 2025-3-29 07:07:43 | 显示全部楼层
0302-9743 vanced Applications, CARDIS 2018, held in Monpellier, France, in November 2018..The 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions...CARDIS has provided a space for security experts from industry and academia to exchange on security of smart ca
发表于 2025-3-29 08:45:49 | 显示全部楼层
发表于 2025-3-29 11:41:21 | 显示全部楼层
Non-profiled Mask Recovery: The Impact of Independent Component Analysis, re-computation schemes as well as the masking scheme in DPAContest V4.2. We propose a novel approach based on Independent Component Analysis (ICA) to efficiently utilise the information from several leakage points to reconstruct the respective masks (for each trace) and show it is a competitive attack vector in practice.
发表于 2025-3-29 15:49:12 | 显示全部楼层
发表于 2025-3-29 22:58:06 | 显示全部楼层
Convolutional Neural Network Based Side-Channel Attacks in Time-Frequency Representations,rts have been paid to develop profiled attacks from Template Attacks to deep learning based attacks. However, most attacks are performed in time domain – may lose frequency domain information. In this paper, to utilize leakage information more effectively, we propose a novel deep learning based side
发表于 2025-3-30 02:10:48 | 显示全部楼层
发表于 2025-3-30 05:48:35 | 显示全部楼层
Improving Side-Channel Analysis Through Semi-supervised Learning,ker) gains access to a profiling device to build a precise model which is used to attack another device in the attacking phase. Mostly, it is assumed that the attacker has significant capabilities in the profiling phase, whereas the attacking phase is very restricted. We step away from this assumpti
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 吾爱论文网 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
QQ|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-8-3 11:26
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表