在SPECK密码中基于深度学习的分析侧通道攻击
Faisal Hameed1,2, Hoda Alkhzaimi3,4
1Tandon School of Engineering, New York University, New York, USA. fah276@nyu.edu.
Scientific reports
|July 18, 2025
概括
本研究提出了一种新的深度学习方法,用于对SPECK密码的侧通道攻击进行分析. 该技术成功地使用最小的数据恢复了SPECK-32/64密钥,提高了物联网安全性.
科学领域:
- 密码学 密码学 密码学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 深度学习分析的侧通道分析正在进步,特别是在AES等经典密码中.
- 这些先进技术适应轻量加密技术,如SPECK密码,尚未得到充分的探索.
- 对于物联网 (IoT) 设备来说,SPECK是至关重要的,需要对侧通道攻击进行强有力的防御.
研究的目的:
- 为 SPECK 轻量级密码引入一种基于深度学习的新型配置文件侧通道分析.
- 为弥补研究中的差距,将先进的深度学习攻击应用于轻量级原始体.
- 评估与SPECK实施相比,深度学习组合方法的有效性.
主要方法:
- 利用了深度学习模型的顺序分割和征服组合.
- 将该技术应用于SPECK-32/64密码的软件实现.
- 专注于分析侧通道分析,以恢复秘密密钥.
主要成果:
- 成功地恢复了SPECK-32/64密码的8字节秘密密钥.
- 在不到250个轨迹内实现了关键回收,证明了高效率.
- 这代表了对未受保护和受保护的SPECK实现的第一个已知的基于深度学习的分析攻击.
结论:
- 深度学习组合方法对于对SPECK等轻量级密码的侧通道攻击进行分析是有效的.
- 拟议的方法在物联网中保护资源有限的设备方面取得了重大进展.
- 需要进一步的研究,以在其他轻量级加密原始体上探索这些技术.
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