NSF Org:	DGE Division Of Graduate Education
Recipient:	GEORGIA STATE UNIVERSITY RESEARCH FOUNDATION INC
Initial Amendment Date:	April 10, 2020
Latest Amendment Date:	April 3, 2023
Award Number:	2020636
Award Instrument:	Standard Grant
Program Manager:	Li Yang liyang@nsf.gov  (703)292-2677 DGE  Division Of Graduate Education EDU  Directorate for STEM Education
Start Date:	April 1, 2020
End Date:	March 31, 2024 (Estimated)
Total Intended Award Amount:	$383,371.00
Total Awarded Amount to Date:	$383,371.00
Funds Obligated to Date:	FY 2018 = $327,037.00
History of Investigator:	Daniel Takabi (Principal Investigator) takabi@odu.edu
Recipient Sponsored Research Office:	Georgia State University Research Foundation, Inc. 58 EDGEWOOD AVE NE ATLANTA GA  US  30303-2921 (404)413-3570
Sponsor Congressional District:	05
Primary Place of Performance:	Georgia State University Research Foundation 58 Edgewood Avenue Atlanta GA  US  30303-2921
Primary Place of Performance Congressional District:	05
Unique Entity Identifier (UEI):	MNS7B9CVKDN7
Parent UEI:
NSF Program(s):	CYBERCORPS: SCHLAR FOR SER
Primary Program Source:	04001819DB NSF Education & Human Resource
Program Reference Code(s):	9179, 9178, 7434, 7254, SMET, 025Z
Program Element Code(s):	166800
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.076

ABSTRACT

Given the wide-spread use of big data, there is a growing need to develop a cyber-workforce that understands cybersecurity in the context of big data. The goal of this project from the University of North Texas is to integrate data science into cybersecurity curriculum and train the next generation of security experts. The project proposes to have direct and long-term impacts on the growing national need for highly-trained cybersecurity professionals with data analytics capabilities, by increasing the number and quality of cybersecurity analysts. This project aims to develop instructional materials that cater to a wide-range of student learning styles. The materials will be designed so that educators at a wide-range of institutions (e.g., community college to research-intensive institutions), and with varying levels of cybersecurity knowledge, can easily incorporate them into their instruction.

The proposed project seeks to develop a set of instructional modules and hands-on labs that make use of state-of-the-art data analytics for addressing different cybersecurity challenges. These instructional modules will follow active learning principles designed to engage students, regardless of learning style, and ensure that students retain the content learned. The modules will be based on real-world security systems and will be designed to systematically cover fundamental security principles. This approach will allow students to get exposure to data analytics techniques and their application to cybersecurity challenges via real-world examples. The project aims to produce engaging materials that could be easily adopted by other educators. To simplify integration and encourage adoption, the hands-on labs will be built based on only open source software and tools that are free to use for educational purposes. Further, they will be distributed via virtual machine images that already contain all libraries and required software to run the labs. This approach for development will allow a variety of instructors to confidently integrate state-of-the-art data analytics labs into curriculum with minimal effort.

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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