中国科学技术大学学报 ›› 2020, Vol. 50 ›› Issue (10): 1343-1358.DOI: 10.3969/j.issn.0253-2778.2020.10.006

• 科研论文 • 上一篇    

基于对抗攻击的侧信道防护方案

  

  1. 中国科学技术大学中国科学院电磁空间信息重点实验室,安徽合肥 230027
  • 收稿日期:2020-10-09 修回日期:2020-10-24 出版日期:2020-10-31 发布日期:2020-12-07

Adversarial attack based countermeasures against deep learning side-channel attacks

  1. CAS Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China, Hefei 230027, China
  • Received:2020-10-09 Revised:2020-10-24 Online:2020-10-31 Published:2020-12-07
  • About author:GU Ruizhe: Master candidate. Research field: side-channel analysis. E-mail: zheruigu@mail.ustc.edu.cn
    Wang Ping: Master candidate. Research field: Information safety. E-mail: wangpingwk@163.com
    Zheng Mengce: PhD candidate. Research field: Cryptanalysis of side channel. E-mail: mczheng@ustc.edu.cn

    Hu Honggang: Corresponding author, PhD/professor. Research field: Cryptography, network security. E-mail: hghu2005@ustc.edu.cn
    Yu Nenghai: PhD/professor. Research field: Video processing and multimedia communication. E-mail: ynh@ustc.edu.cn

摘要: 随着深度学习技术在侧信道攻击领域的广泛应用,基于深度学习的侧信道攻击对现代密码设备的威胁越来越大. 现有的侧信道防护方案是针对经典的侧信道攻击而设计的,不能保护密码设备免受基于深度学习的侧信道攻击,因此亟需一个针对基于深度学习的侧信道攻击的防护对策. 尽管深度学习在解决复杂问题方面具有很高的潜力,但它很容易受到对输入添加轻微扰动形式的对抗攻击,从而导致模型误分类. 为此提出了一种基于对抗攻击的新颖侧信道防护对策,专门针对基于深度学习的侧信道攻击. 实验表明,该防护方案可以有效地保护密码设备免受基于深度学习的侧信道攻击和传统的侧信道攻击的威胁.

关键词: 侧信道攻击, 侧信道防护, 对抗攻击, 深度学习

Abstract: Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. These studies show that modern cryptographic devices are increasingly threatened by side-channel attacks with the help of deep learning. However, the existing countermeasures are designed to resist classical side-channel attacks, and cannot protect cryptographic devices from deep learning based side-channel attacks. Thus, there arises a strong need for countermeasures against deep learning based side-channel attacks. Although deep learning has the high potential in solving complex problems, it is vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to give wrong pedictions.  In this paper,  a kind of novel countermeasures is proposed based on adversarial attacks that is specifically designed against deep learning based side-channel attacks. We estimate several models commonly used in deep learning based side-channel attacks to evaluate the proposed countermeasures. It is shown that our approach can effectively protect cryptographic devices from deep learning based side-channel attacks in practice. In addition, our experiments show that the new countermeasures can also resist classical side-channel attacks.

Key words: side-channel attacks, countermeasures,  , adversarial attack, deep learning

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