NSF award 2451231 · Northern Arizona University

CRII: SaTC: Adversarial Malware Generation: A Universal Evasion Framework Targeting Neural Network-based Malware Detectors

Final report due

28 January 2028

in 467 days · The award ends on 30 September 2027.

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award ending 2027-09-30 · Research.gov
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Extension request16 Aug 2027in 302 days
award ending 2027-09-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Sep 2027in 337 days
award ending 2027-09-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Jan 2028in 467 days
award ending 2027-09-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Jan 2028in 467 days
award ending 2027-09-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Jan 2028in 467 days
award ending 2027-09-30 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 30 September 2027.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 January 2028.

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Award card

Award number: 2451231
Period of support: 1 Oct 2025 – 30 Sep 2027
Amount: $174,103
Awardee: Northern Arizona University, Flagstaff, AZ
Program: Secure &Trustworthy Cyberspace
Directorate: CSE

Abstract

This project’s goal is to improve the robustness of machine learning models used to detect malicious software, or “malware”. Computers are constantly vulnerable to malware, which can steal sensitive information, disrupt operations, or damage systems. Detecting and preventing malware is a significant challenge, and although recent advancements in deep learning have made it easier to identify potential malware…

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