# CRII: SaTC: Towards Data-effective and Cost-efficient Security Attack Detections

> **NSF 01002324DB NSF RESEARCH & RELATED ACTIVIT** · Rochester Institute of Tech (NY) · $174,943

## Abstract

Increased connectivity of devices and people to the Internet has created an ever-expanding security attack surface. Machine learning (ML) techniques have been used to help detect attacks and may offer a more scalable way to deal with an increasingly large attack surface. However, acquiring a large volume of high-quality labelled attack samples is both costly and time consuming. Further, the acquired data set quite often do not fully represent the true data distribution. Given the challenge of labeled data scarcity and imbalance in representation, this project's novelties are to explore new ways to build data driven cyber-attack detection systems that can learn effectively from limited or biased cyber data set in a cost-efficient manner. The project's broader significance and importance are 1) enhancing the data-driven security attack detection infrastructure that leads to more secure and trustworthy cyberspace; 2) bridging the gap between research and practice by creating open-source systems that encourage real security productions, 3) providing research opportunities to both undergraduate and graduate students in the area of AI/ML enabled cyber defense.

This project unveils an insight on how limited and/or imbalanced attack samples can be used as effective training data to facilitate data-driven model construction and enable high-performance security attack detection with low cost in practice. Towards this insight, this project contains three technical approaches: (1) c

## Key facts

- **NSF award ID:** 2549995
- **Awardee organization:** Rochester Institute of Tech (NY)
- **SAM.gov UEI:** J6TWTRKC1X14
- **PI:** Lingwei Chen
- **Primary program:** 01002324DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** SaTC: Secure and Trustworthy Cyberspace, COVID-Disproportionate Impcts Inst-Indiv, CISE Resrch Initiatn Initiatve
- **Estimated total:** $174,943
- **Funds obligated:** $117,019
- **Transaction type:** Standard Grant
- **Period:** 08/15/2025 → 02/28/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2549995

## Citation

> US National Science Foundation, Award 2549995, CRII: SaTC: Towards Data-effective and Cost-efficient Security Attack Detections. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2549995. Licensed CC0.

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