Project Grant 2153358
- This $255,391 National Science Foundation Technology, Innovation, and Partnerships grant funds Anomalee Inc. to develop a commercially viable prototype for certifying deep neural networks against backdoor attacks. Backdoor attacks maliciously poison AI models to misclassify inputs containing hidden trigger patterns. Anomalee's unsupervised method will mathematically evaluate DNNs for such vulnerabilities without examples of poisoned models or access to training data, addressing a critical need...
- 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...
- The National Science Foundation (NSF) awarded a $402,229 Computer and Information Science and Engineering (CISE) Program grant to The Research Foundation For The State University Of New York, doing business as Stony Brook University. This 1-year grant, effective November 1, 2023, supports research and development focused on improving the security of machine learning (ML) systems that leverage third-party, pre-trained models. The project aims to develop rigorous methods for detecting and...
- 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) awarded a $240,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Central Florida (UCF) Board of Trustees Office of Research. The grant supports a 3-year research project to develop a theoretical analysis that sheds light on the robustness of neural network-based methods and the properties of adversarial training. The research aims to contribute to the development of more robust neural network-based...
- 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...
- The National Science Foundation awarded a $300,000 project grant to Princeton University under the Computer and Information Science and Engineering program to support research towards securing federated learning. Over a four-year period ending September 2026, Princeton researchers will investigate security vulnerabilities in the training phase of federated learning models, develop provably secure federated learning methods to prevent poisoning and backdoor attacks, and create techniques to...
- This $300,000 National Science Foundation project grant supports research into robust machine learning under sparse adversarial attacks through 2025. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the University of California, Santa Barbara will develop theoretical frameworks and defense methods to make machine learning models resilient against perturbations affecting few data points. Specifically, the researchers aim to establish fundamental limits of...
- 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 National Science Foundation project grant of $599,999 will fund research at Duke University from October 2022 through September 2026 towards developing secure methods for federated learning. Federated learning is an emerging machine learning technique that allows analysis of private data without centralized collection, but current methods lack security protections. Under the Computer and Information Science and Engineering program (CFDA 47.070), the researchers will explore new security...
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 models. They will also devise robust backdoor eradication schemes for guaranteed model recovery and investigate preventive defenses to make backdoors harder to form during training. In parallel, the investigator will establish a neural backdoor testbed environment collecting relevant libraries and datasets. This testbed aims to support standardized, replicable backdoor and neural network security research overall. The work intends to deliver enabling detection and prevention technologies to secure deep learning applications and accelerate their trustworthy adoption across various domains.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $175.0k | 2/3/22 |