The National Science Foundation (NSF) is providing a $206,382 Project Grant under its Computer and Information Science and Engineering (CISE) program to the Illinois Institute of Technology (IIT) Sponsored Research and Programs Division. The objective of this 5-year award is to design a trustworthy, flexible, and generalizable machine learning framework that can provide robustness against common privacy and security attacks. The project will develop novel information-theoretic representation...
This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program, totaling $247,903, supports research from November 2021 through September 2024 to develop techniques for improving the adaptability of machine learning-based security defenses. The goal is to enable these defenses to better handle dynamic changes in data caused by evolving attacks and changes in benign system usage, with reduced need for costly manual data labeling. The awardee,...
This $600,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to advance the security and privacy of on-device machine learning (ML) models for scientific applications in Internet of Things (IoT)-driven cyberinfrastructure (CI). The project has two primary objectives: 1) Developing a novel runtime detection and prevention mechanism for ML model extraction attacks in CI applications,...
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 $242,024 Project Grant awarded by the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) to Carnegie Mellon University (CMU) focuses on improving the reliability and efficiency of machine learning (ML) models for detecting malicious software (malware). The project aims to develop new techniques to make ML-based malware detectors more resistant to being fooled by attackers, as well as more time- and space-efficient. Key innovations include novel...
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 Project Grant award of $400,000 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to enhance digital forensics capabilities for analyzing and investigating attacks on machine learning (ML) models. The primary objectives are to: 1) Develop forensic tools for reconstructing HDF5 model files and assess their data recovery precision; 2) Evaluate data injection detection methods in HDF5 models; and 3) Design and appraise...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $391,848 to Purdue University to develop holistic systems for securing the machine learning supply chain. The project aims to create tools to quantify trust in machine learning supply chains and verify security requirements across those supply chains. The research will also support the development of a diverse next generation of computer...
The National Science Foundation awarded The Johns Hopkins University a $900,000 Project Grant under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to conduct collaborative research focused on understanding robustness in machine learning via parsimonious structures from October 1, 2022 to September 30, 2025. Specifically, the University will research conditions under which one can detect adversarial attacks on networks or data poisoning and reconstruct...
This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $246,643 to Penn State University for research on detecting and mitigating non-functional vulnerabilities in machine learning libraries. Specifically, the university will develop methods to detect denial-of-service vulnerabilities and quantify side-channel vulnerabilities in core ML libraries such...