Project Grant 2212010
- This $194,726 two-year Project Grant from the National Science Foundation's Engineering program (CFDA 47.041) will fund research at Worcester Polytechnic Institute on machine learning techniques for assessing hardware security against side-channel attacks. The grantee will develop novel approaches using deep learning theoretical foundations to evaluate cryptosystems' resilience to machine learning-enhanced side-channel analysis on real-world implementations. The research aims to address...
- This $250,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at Northeastern University to improve the security of machine learning models in multi-tenant cloud field programmable gate array (FPGA) environments. The three-year award aims to advance understanding of vulnerabilities in cloud-FPGAs shared by multiple tenants, where a malicious actor could potentially manipulate another tenant's machine learning...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $174,998 to The Trustees of the Stevens Institute of Technology to investigate timing side channels in adaptive neural networks. The project aims to: (1) define a threat model for exploiting timing channels to gain sensitive user information, (2) develop a machine learning-based pipeline to utilize these timing channels, (3) create an...
- This $175,000 two-year project grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at Old Dominion University Research Foundation to advance secure deep learning systems. The awardee will systematically study existing neural network backdoor attacks to understand fundamental attack principles. Based on these findings, the research team will develop algorithms to accurately detect neural backdoors embedded in deep learning...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with a total funding of $122,767, supports a research project by the University of California, Berkeley aimed at hardening off-the-shelf software against side channel attacks. The project consists of three main tasks: (1) reverse engineering hardware structures to determine potential attack vectors, (2) designing software transformations to protect against side...
- This three-year, $483,326 National Science Foundation project grant funds research at the University of Florida to develop countermeasures against laser-assisted side-channel attacks on integrated circuits. Side-channel attacks extract secret keys and data from chips using probing techniques, compromising systems in critical infrastructure, autonomous vehicles, aerospace and defense. The researchers will investigate "Eradicator," a multi-layer suite of sensors, randomization and tamper...
- This four-year $300,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop secure foundations for federated learning. Federated learning enables machine learning models to be collaboratively trained using data from many client devices without sharing private information. The researchers will investigate security vulnerabilities in federated learning's training phase, such as poisoning and backdoor attacks. They will...
- The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) federal grant program (CFDA 47.070) awarded a $148,347 project grant to the University of Maine System (UMS) to develop a new framework for microelectronic security and trust. The key elements of the project are: Using reinforcement learning techniques to discover novel attack vectors against logic locking and identify root causes behind successful attacks. Leveraging explainable artificial intelligence...
- This three-year, $425,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will fund research at The Pennsylvania State University to develop provably secure, online learning-based adaptive cyber defenses for real-world servers. The research aims to provide mathematically rigorous guarantees by synthesizing new models integrating machine learning, game theory, and control theory. It will...
- The National Science Foundation Division of Computer and Network Systems awarded a $731,838 Project Grant to the University of Texas at Dallas to develop algorithms and techniques for learning the hidden structure of digital and analog circuits from side-channel measurements. This work aims to advance hardware security and integrity checking by allowing adaptive interaction with circuits to intelligently model arbitrary analog circuit side-channels and utilize non-linear solvers and...
This two-year, $600,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to advance side-channel analysis and protection through deep learning techniques. Funded research at Northeastern University will develop novel methods for applying deep neural networks to both microarchitectural side-channel attacks and effective countermeasures. Key products include a persistent cache monitoring mechanism to improve observation of victim execution for secret retrieval. Researchers will build appropriate deep learning models to exploit high-quality timing traces from this mechanism. The grant also supports exploring cross-device transfer learning and generative adversarial networks to apply the attack framework across diverse platforms. Researchers will leverage adversarial examples to guide new countermeasures against deep learning-based side-channel attacks. Outcomes aim to significantly strengthen security of cloud and critical systems through enhanced side-channel evaluation and protections using deep learning methods.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 6/29/22 |