Unclonable photonic keys hardened against machine learning attacks
Unclonable photonic keys hardened against machine learning attacks
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DOI：
10.1063/1.5100178
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发表时间：
2020-01-01
期刊：
影响因子：
5.6
通讯作者：
Foster, Amy C.
中科院分区：
文献类型：
--
作者：
Bosworth, Bryan T.;Atakhodjaev, Iskandar A.;Foster, Amy C.
信息时代的标志是信息在整个地球仪中存储、访问和共享的便利性。这在很大程度上是由于复制数字信息的简单性而没有错误。不幸的是，一个日益严重的后果是我们对数字依赖所带来的全球安全和隐私威胁。具体地，现代安全通信和认证遭受由存储在数字媒体中的秘密密钥的复制的可能性引起的可怕的威胁。由于信息传输相对较少，攻击者可以冒充合法用户，发布被数百万台计算机自动接受为安全的恶意软件，或者窃听无数的数字交换。为了解决这一漏洞，正在开发一种称为物理不可克隆函数（PUF）的新型加密设备。PUF是物理密钥这一古老概念的现代实现，为数字密钥存储提供了一种有吸引力的替代方案。用户从PUF的物理行为导出数字密钥，该物理行为对超出制造公差的物理特性敏感。因此，与传统的物理密钥不同，PUF不能被复制，并且只有保持器可以提取数字密钥。然而，新兴的机器学习（ML）方法非常擅长通过训练来学习行为，如果这些算法可以学习模仿PUF，那么安全性就会受到影响。不幸的是，这种攻击对传统的电子PUF非常成功。在这里，我们研究ML攻击对非线性硅光子PUF，一种新颖的设计，利用非线性光学相互作用在混乱的硅微腔。首先，我们调查了这些设备在制造过程中对克隆的抵抗力，并展示了它们作为大量加密密钥材料来源的用途。接下来，我们证明了硅光子PUF由于其非线性而表现出对最先进的ML攻击的抵抗力，并最终在加密场景中验证了这种抵抗力。(C)2020年作者。
The hallmark of the information age is the ease with which information is stored, accessed, and shared throughout the globe. This is enabled, in large part, by the simplicity of duplicating digital information without error. Unfortunately, an ever-growing consequence is the global threat to security and privacy enabled by our digital reliance. Specifically, modern secure communications and authentication suffer from formidable threats arising from the potential for copying of secret keys stored in digital media. With relatively little transfer of information, an attacker can impersonate a legitimate user, publish malicious software that is automatically accepted as safe by millions of computers, or eavesdrop on countless digital exchanges. To address this vulnerability, a new class of cryptographic devices known as physical unclonable functions (PUFs) are being developed. PUFs are modern realizations of an ancient concept, the physical key, and offer an attractive alternative for digital key storage. A user derives a digital key from the PUF's physical behavior, which is sensitive to physical idiosyncrasies that are beyond fabrication tolerances. Thus, unlike conventional physical keys, a PUF cannot be duplicated and only the holder can extract the digital key. However, emerging machine learning (ML) methods are remarkably adept at learning behavior via training, and if such algorithms can learn to emulate a PUF, then the security is compromised. Unfortunately, such attacks are highly successful against conventional electronic PUFs. Here, we investigate ML attacks against a nonlinear silicon photonic PUF, a novel design that leverages nonlinear optical interactions in chaotic silicon microcavities. First, we investigate these devices' resistance to cloning during fabrication and demonstrate their use as a source of large volumes of cryptographic key material. Next, we demonstrate that silicon photonic PUFs exhibit resistance to state-of-the-art ML attacks due to their nonlinearity and finally validate this resistance in an encryption scenario. (C) 2020 Author(s).