EAGER: Collaborative: Machine-Learning based Side-Channel Attack and Hardware Countermeasures

EAGER：协作：基于机器学习的侧通道攻击和硬件对策

• 批准号: 1935534
• 负责人: Arijit Raychowdhury
• 金额: $ 15万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935534&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Machine; Learning; based

Digital Encryption is typically performed by specialized circuits to ensure confidentiality and integrity of data. While encryption is mathematically robust, the circuits encrypting data may leak information via the amount of the power drawn from the supply, and the amount of electromagnetic (EM) radiation that emanates from the circuit. This is known as side-channel leakage. An attacker may be able to unravel the secret cryptographic information by analyzing the side-channel leakage, thereby compromising security. Newer analysis techniques based on machine-learning make the attack easier. This proposal will study how these attacks are performed to develop means of protection against such attacks.Machine learning (ML) based side-channel attack (SCA) increases the attack surface of secure hardware, as an attacker can potentially compromise a device using a few power and EM traces. This proposal will provide a comprehensive analysis for new attack models and countermeasures through: (1) analysis and development of the best possible Deep Learning based SCA attack (on power and EM). (2) Design and demonstrate low-overhead countermeasures to enable "critical" crypto signature attenuation and reduce the signal-to-noise ratio by a factor of 500. The goal of this proposal is to develop end-to-end models to build defense mechanisms for both protected and unprotected Advanced Encryption Standard implementations. Results from this project will be disseminated through papers and articles, which will be made publicly available. Results from this project will be incorporated into the courses taught by the investigators. Investigators will seek to work with undergraduate students providing hands-on experience on cryptography and side-channel attacks and analysis.The data generated from this project will be in the form of simulation results and models, software tools and hardware measurements. The developed models and benchmarking software will be uploaded to GitHub at: https://github.com/anupamgolder/mlscaThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数字加密通常由专用电路执行，以确保数据的机密性和完整性。虽然加密在数学上是鲁棒的，但是加密数据的电路可能经由从电源汲取的功率的量以及从电路发出的电磁（EM）辐射的量来泄漏信息。这被称为侧通道泄漏。攻击者可能能够通过分析侧信道泄漏来解开秘密密码信息，从而危及安全性。基于机器学习的新分析技术使攻击更容易。该提案将研究这些攻击是如何执行的，以开发针对此类攻击的保护手段。基于机器学习（ML）的侧信道攻击（SCA）增加了安全硬件的攻击面，因为攻击者可以使用一些电源和EM痕迹来破坏设备。该提案将通过以下方式为新的攻击模型和对策提供全面的分析：（1）分析和开发最佳的基于深度学习的SCA攻击（对电源和EM）。(2)设计并演示低开销对策，以实现“关键”加密签名衰减，并将信噪比降低500倍。本提案的目标是开发端到端模型，为受保护和不受保护的高级加密标准实现构建防御机制。该项目的成果将通过公开发表的论文和文章传播。该项目的成果将纳入调查人员讲授的课程。研究人员将寻求与本科生合作，提供密码学和侧信道攻击和分析方面的实践经验。该项目产生的数据将以模拟结果和模型、软件工具和硬件测量的形式出现。开发的模型和基准测试软件将上传到GitHub：https://github.com/anupamgolder/mlscaThis奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。