| NSF Org: |
DGE Division Of Graduate Education |
| Recipient: |
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| Initial Amendment Date: | July 27, 2020 |
| Latest Amendment Date: | July 27, 2020 |
| Award Number: | 2039542 |
| 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: | August 1, 2020 |
| End Date: | July 31, 2024 (Estimated) |
| Total Intended Award Amount: | $239,855.00 |
| Total Awarded Amount to Date: | $239,855.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
800 WEST CAMPBELL RD. RICHARDSON TX US 75080-3021 (972)883-2313 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
800 West Cambell Richardson TX US 75080-3021 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Secure &Trustworthy Cyberspace |
| Primary Program Source: |
04002021DB NSF Education & Human Resource |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.076 |
ABSTRACT
In recent years, researchers have applied artificial intelligence (AI) to effectively solve important problems in cybersecurity. While significant research progress has been made in cybersecurity with the help of AI, there is a shortage of highly educated workers who can solve challenging problems at the intersection of AI and cybersecurity. This project will develop such a workforce by educating qualified individuals from diverse communities in cybersecurity and AI simultaneously. The project team will develop and deliver modular and project-based courses for graduate students that cover the basics of AI and cybersecurity using real-life problems. The development of innovative courses is intended to strengthen the student experience and to build a strong and diverse workforce in AI and cybersecurity that will fill the current voids in government, industry, and academia.
The project team will develop five modular courses for graduate students: (1) Scalable Advanced Analytics, (2) AI including Explainable Machine Learning (ML), (3) ML for Cybersecurity, (4) Cybersecurity for ML (e.g., Adversarial ML), and (5) Secure Blockchain Technologies. The design of these modular and hybrid courses will incorporate research-based pedagogies and innovative technologies. Courses will be offered in both instructor-led and student-directed learning formats to study the differences in learning outcome, if any, between these two different approaches. This project will provide important information regarding optimal methods to deliver interdisciplinary cybersecurity curricula and how the education community can effectively broaden access to cybersecurity education beyond typical classroom courses. The project team will conduct outreach activities to ensure participation by underrepresented populations and will disseminate findings through workshops at relevant meetings of professional societies.
This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Project Outcomes Report
The Cyber Security (CyS) project was initiated to address the growing need for enhanced protection of critical technological assets, such as banks, hospitals, and infrastructures, from the threats posed by hackers. As our dependence on computing systems continues to rise, it has become imperative to develop a highly skilled cyber security workforce capable of tackling complex challenges. This project aimed to bridge the gap by integrating Cyber Security (CyS) with Artificial Intelligence (AI), thereby equipping a diverse range of students with the necessary skills to defend against emerging cyber threats.
Major Goals:
The project focused on several key goals:
1. Development of Modular Courses: We aimed to create and offer modularized, project-based courses that would educate students in both CyS and AI. These courses include Scalable Advanced Analytics, AI including Explainable Machine Learning, Cybersecurity for Machine Learning, Machine Learning for Cybersecurity and Secure Blockchain Technologies.
2. Workforce Development: By creating specialized courses and employing research-based pedagogies, the project sought to develop a workforce proficient in addressing the intersection of AI and CyS.
Key Accomplishments:
1. Course Implementation and Content Development:
- Scalable Advanced Analytics: This course has been modularized into key components such as “Feature Extraction” and “Predictive Analytics,” with practical assignments including homework and quizzes.
- AI including Explainable Machine Learning: The course was updated with new content on adversarial machine learning, providing an essential introduction to concepts like evasion attacks and data poisoning.
- Secure Blockchain Technologies: New modules introduced include smart contract programming and detailed explorations of Bitcoin and Ethereum network protocols. These updates provide students with practical skills in developing and utilizing blockchain technologies.
- Cybersecurity for Machine Learning: This course was enhanced with introductory materials on adversarial machine learning, improving accessibility for both beginners and experienced learners.
- Machine Learning for Cybersecurity: This course covered a broad range of core topics, including fundamental machine learning concepts such as classification techniques and feature extraction, as well as basics of Big Data, including an introduction to Spark and Hadoop. Additionally, it addressed the application of machine learning techniques to solve real-world cybersecurity challenges, such as KillChain Phase classification and threat report classification.
2. Enrollment and Accessibility:
- Courses are hosted on the University of Texas at Dallas (UTD) e-Learning platform and are accessible to both UTD students and a diverse group of external participants. Enrollment numbers vary by course, with significant interest in Secure Blockchain Technologies and AI including Explainable Machine Learning.
3. Student Feedback and Satisfaction:
- Data collected over multiple semesters showed high satisfaction with video lectures and an overall improvement in student satisfaction with course methods. The decrease in negative feedback and increased ratings for multimedia tools indicate that the course modules have been well-received and effective.
Significant Results and Impact:
The project has made notable strides in both intellectual merit and broader impacts:
- Intellectual Merit: The integration of AI and CyS in course design has provided students with cutting-edge knowledge and skills, addressing the current shortage of qualified professionals in these critical fields. The modular approach and innovative pedagogies used have set a new standard for course development in these areas.
- Broader Impacts: By educating a diverse group of individuals and offering flexible, technology-enhanced learning opportunities, the project has contributed to building a robust cyber security workforce. The updated course content, particularly in blockchain and AI, prepares students to tackle real-world challenges and advances the progress of science in these emerging fields.
Overall, this project has successfully met its goals by enhancing educational offerings in CyS and AI, fostering a skilled workforce, and making a positive impact on the broader field of cyber security.
Last Modified: 10/31/2024
Modified by: Latifur R Khan
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