This $151,946 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of "DANGER-IOT", an approach to detecting malware across heterogeneous Internet-of-Things (IoT) platforms. The key products and services being delivered through this 3-year award, which runs from October 2024 to September 2027, include: Creating a generic machine learning model that can detect malware across diverse IoT devices and operating systems by constructing a common embedding space for similar functions. Optimizing the model for efficiency on low-power IoT devices through techniques like model compression and pruning. Enhancing the robustness of the malware detection approach against advanced attacks that aim to evade detection, such as by exploring large language models for code-style transfer. Integrating multi-task learning, behavior classification, and a comprehensive IoT malware dataset to provide a scalable, defensive solution. Sharing open-source tools, benchmarks, and datasets developed through the project to broadly contribute to the IoT security research community. The project is a collaborative effort across four universities in three countries, including Northeastern University as the lead institution, and is designed to have significant broader impacts through new courses, security competitions, and international student exchanges.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $151.9k | 3/4/25 |