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Minimize RSR Award Detail

Research Spending & Results

Award Detail

  • Francesca Spezzano
  • Edoardo Serra
Award Date:12/17/2019
Estimated Total Award Amount: $ 364,500
Funds Obligated to Date: $ 364,500
  • FY 2020=$364,500
Start Date:02/01/2020
End Date:01/31/2023
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:REU Site: Data-driven Security
Federal Award ID Number:1950599
DUNS ID:072995848
Parent DUNS ID:072995848
Program Officer:
  • Joseph Maurice Rojas
  • (703) 292-8455

Awardee Location

Street:1910 University Drive
Awardee Cong. District:02

Primary Place of Performance

Organization Name:Boise State University
Street:1910 University Dr
Cong. District:02

Abstract at Time of Award

This award establishes a new Research Experiences for Undergraduates (REU) Site focused on data-driven security at Boise State University. Data-driven security is an emerging interdisciplinary field that applies data science and artificial intelligence to mitigate cyberattacks and other security risks and threats. Undergraduate students will participate in summer research activities with faculty mentors from the computer science and mathematics disciplines. The students will work in teams to explore important research questions and will also participate in other professional development activities that will prepare them for future careers in the computing fields. The site will target students from groups traditionally under-represented in computer science as well as students from two-year colleges in the Pacific Northwest. The REU Site will feature transformative interdisciplinary research in the field of data-driven security. Research problems include: a novel data-driven approach to learn the characteristic function of a coalition game that models a covert network to improve key actor identification; new approaches to detect misbehavior and mitigate misinformation; innovative robust traffic-flow behavioral model extraction for detecting anomalous situations in enterprise networks; development of new lightweight cryptographic algorithms that are robust to both algorithmic weaknesses and side-channel attacks performed with machine learning. All of the research requires deep integration of data science, artificial intelligence, mathematics, and security. Students will learn to work in teams and communicate their results to a diverse audience by participating in activities that use investigative methodologies based on constructive dialogue and collaborative design. The projects have the potential to broaden knowledge in several domains, including game theory, intrusion detection systems, misinformation mitigation, and lightweight cryptography. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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