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 National Science Foundation (NSF) grant award under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $204,962 to Duke University to develop machine learning models that can reliably detect malware without running it. The key innovations of this 3-year project include new techniques to improve the resistance of malware detectors to evasion attempts, as well as more efficient model architectures. The project aims to open-source these advances where...
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 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $524,981 to Northeastern University to develop novel methods and tools for investigating and defending against machine learning (ML) poisoning attacks on cyber networks. The project aims to create explanation-based ML and generative modeling techniques to identify stealthy poisoning attacks against supervised, semi-supervised, and...
This Project Grant award of $151,946 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of DANGER-IOT, an approach to detecting malware across heterogeneous Internet-of-Things (IoT) systems. Led by Northeastern University, the project aims to create a generic machine learning model that can detect malware on diverse IoT platforms, while ensuring efficiency for low-power devices and robustness against advanced...
This Project Grant award of $500,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research to understand and mitigate security vulnerabilities in machine learning (ML) models. The research aims to characterize how malicious actors could exploit the unused parameters in trained ML models to install covert functionality, and develop mitigation approaches to improve the robustness and trustworthiness of ML...
This $316,672 Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research at San Diego State University Foundation to develop a comprehensive understanding of the robustness and computational efficiency of deep neural networks. The key focus areas include: 1) formulating theoretical frameworks for subnetwork adversarial robustness, 2) characterizing transferability through curriculum learning, and 3)...
This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) provides $128,737.00 to The Trustees of Princeton University to develop NETFORTIFY, an open-source framework for testing and strengthening the reliability of machine learning (ML)-powered networking functions. The key objectives are to: (1) define formal semantics for "contextual robustness" to ensure ML-based networking approaches meet required properties...
This Project Grant award of $600,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a security-focused framework to protect collaborative scientific computing in machine learning as a service (MLaaS) environments. The key research thrusts of the project include: Robust model protection techniques to hinder reverse engineering of machine learning models while preserving their utility. Behavioral monitoring...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to improve the security and resilience of machine learning (ML) software. The $262,828 award to the University of Illinois will develop methods for detecting and correcting non-functional vulnerabilities in ML libraries, such as denial-of-service attacks and side-channel attacks, which pose security risks beyond just impacting core prediction...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will provide $174,103 to Northern Arizona University (NAU) to develop a framework for improving the robustness of machine learning models used to detect malicious software or "malware". The primary goals of the project are to:
Understand the key features learned by different neural network architectures applied to diverse input domains for malware detection.
Generate new adversarial malware samples that can evade detection by strategically manipulating code based on the identified features.
Investigate how binary rewriting techniques impact the performance of neural network models to improve the robustness of malware detection systems.
The project will use a reinforcement learning-based approach to modify raw binary code to bypass detection by multiple deep learning models. The resulting dataset, techniques, and source code will be openly shared with the research community to contribute to the broader field of malware analysis. This award spans the period from October 1, 2025 to September 30, 2027.