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, Northwestern University, and its researchers will first measure concept drift in malware samples longitudinal data to characterize patterns. They will then develop reactive methods using contrastive learning for detecting drifting samples and prioritizing those for efficient labeling. Finally, the researchers will create proactive defenses using adversarial generative models to synthesize richer training data and labels that mimic future attacker mutations. The work aims to provide tools for measuring, detecting, and mitigating concept drift to strengthen learning-based defenses for malware analysis, intrusion detection, and other uses over the long term.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $247.9k | 4/14/22 |