TWC SBE: Medium: Collaborative: Brain Hacking: Assessing Psychological and Computational Vulnerabilities in Brain-based Biometrics

TWC SBE：媒介：协作：大脑黑客：评估基于大脑的生物识别技术中的心理和计算漏洞

• 批准号: 1564104
• 负责人: Wenyao Xu
• 金额: $ 30万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564104&HistoricalAwards=false
• 关键词: TWC; SBE; Medium; Collaborative; Brain

In September of 2015, it was reported that hackers had stolen the fingerprint records of 5.6 million U.S. federal employees from the Office of Personnel Management (OPM). This was a severe security breach, and it is an even bigger problem because those fingerprints are now permanently compromised and the users cannot generate new fingerprints. This breach demonstrates two challenging facts about the current cybersecurity landscape. First, biometric credentials are vulnerable to compromise. And, second, biometrics that cannot be replaced if stolen are even more vulnerable to theft. This research will investigate a new type of biometric that avoids both of these problems. In particular, the research will evaluate the strengths and weaknesses of brain biometrics. Brain biometrics are more difficult to steal than fingerprints, since current technology for collecting brain biometrics is impossible to use without a person's knowledge and consent. Brain biometrics, importantly, can also be cancelled if stolen. This is because there are vast networks of the brain that generate unique activity, meaning that if a person's brainprint is stolen, they can generate a new one by tapping in to a different brain network. This investigation holds the potential to transform existing authentication systems into more secure and attack-resistant brain biometric solutions; critical for high-security applications.Brain biometrics have recently been shown to be 100% accurate in identifying people, in a pool of 50 users and across a period of up to a year. This research project will systematically evaluate the potential vulnerabilities of brainprint biometrics, with the goals of 1) demonstrating the resistance and robustness of brainprints to the most likely attacks and 2) developing a comprehensive protection plan addressed at the most vulnerable aspects of this method. In particular, the interdisciplinary team plans to investigate psychological and computational attacks. Psychological attacks consist of attempting to force a user to provide their brainprints under duress, or attempting to impersonate a target brainprint through biofeedback entrainment process. Computational attacks consist of attempting to circumvent brainprint authentication system through presenting a counterfeit or stolen brainprint, with varying levels of obfuscation, such as the addition of noise, and attacking the stimuli database. This project will examine potential vulnerabilities in brain biometrics at an unprecedented level of detail, and convert the resulting knowledge into recommendations for implementation of brain biometrics to guard an increasingly vulnerable cyberspace.

2015年9月，有报道称黑客从美国人事管理办公室窃取了560万名联邦雇员的指纹记录。这是一个严重的安全漏洞，而且它是一个更大的问题，因为这些指纹现在已经永久受损，用户无法生成新的指纹。这一漏洞表明了当前网络安全领域的两个具有挑战性的事实。首先，生物识别凭证很容易遭到破坏。其次，生物识别技术一旦被盗就无法被替换，因此更容易被盗。这项研究将探索一种新型的生物识别技术，避免这两个问题。特别是，该研究将评估大脑生物识别技术的优缺点。大脑生物特征比指纹更难窃取，因为目前收集大脑生物特征的技术是不可能在没有个人知情和同意的情况下使用的。重要的是，如果被盗，大脑生物识别技术也可以取消。这是因为大脑中有巨大的网络可以产生独特的活动，这意味着如果一个人的大脑指纹被盗，他们可以通过接入不同的大脑网络来产生新的指纹。这项研究有可能将现有的身份验证系统转变为更安全、更抗攻击的大脑生物识别解决方案；对于高安全性应用程序至关重要。大脑生物识别技术最近被证明在长达一年的时间里，对50名用户的识别是100%准确的。该研究项目将系统地评估脑指纹生物识别技术的潜在漏洞，其目标是：1)展示脑指纹对最可能的攻击的抵抗力和稳健性；2)针对该方法最脆弱的方面制定全面的保护计划。特别是，跨学科团队计划调查心理和计算机攻击。心理攻击包括试图强迫用户在胁迫下提供他们的大脑指纹，或试图通过生物反馈夹带过程模拟目标的大脑指纹。计算攻击包括试图通过提供伪造或被盗的脑指纹来绕过脑指纹认证系统，并使用不同程度的混淆，例如添加噪声，以及攻击刺激数据库。该项目将以前所未有的详细程度检查脑生物识别技术的潜在漏洞，并将所得知识转化为实施脑生物识别技术的建议，以保护日益脆弱的网络空间。

项目成果

Exploring Eye Adaptation in Head-Mounted Display for Energy Efficient Smartphone Virtual Reality

• DOI: 10.1145/3177102.3177121
• 发表时间: 2018-02
• 期刊: Proceedings of the 19th International Workshop on Mobile Computing Systems & Applications
• 作者: Zhisheng Yan;Chen Song;Feng Lin;Wenyao Xu