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

数字加密通常由专门的电路执行，以确保数据的机密性和完整性。虽然加密在数学上是可靠的，但加密数据的电路可能会通过从电源中获取的功率和从电路中发出的电磁（EM）辐射量泄露信息。这就是所谓的侧通道泄漏。攻击者可以通过分析侧信道泄漏来解开秘密的加密信息，从而危及安全性。基于机器学习的新分析技术使攻击变得更容易。本提案将研究这些攻击是如何进行的，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少量电源和电磁迹线潜在地破坏设备。本提案将通过以下方式对新的攻击模型和对策进行全面分析：(1)分析和开发基于深度学习的最佳SCA攻击（针对电源和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，为受保护和未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文件和文章进行传播，并将向公众提供。这个项目的结果将被纳入研究者所教授的课程中。调查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呈现。开发的模型和基准测试软件将上传到GitHub: https://github.com/anupamgolder/mlscaThis。该奖项反映了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。

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.