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Integrating Artificial Intelligence into Cybersecurity Curriculum: New Perspectives

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Conference

2022 ASEE Annual Conference & Exposition

Location

Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

Computers in Education 1 - Programming I

Page Count

15

DOI

10.18260/1-2--41761

Permanent URL

https://peer.asee.org/41761

Download Count

575

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Paper Authors

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AHMET ARIS Florida International University

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Ahmet Aris is a Research Assistant Professor in the Department of Electrical and Computer Engineering at Florida International University. He is conducting research in Cyber-Physical Systems Security Lab (CSL) at Florida International University under the supervision of Dr. A. Selcuk Uluagac. He earned both PhD and MSc. in Computer Engineering from the Graduate School of Science, Engineering and Technology at Istanbul Technical University, Turkey. He also worked at Medianova CDN R\&D Center as an R\&D Analyst. In addition, he conducted research in the Networked Embedded Systems (NES) Group at Swedish Institute of Computer Science (SICS) as a visiting researcher. His research interests include IoT Security, Network Security, Web Security, and Malware.

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Luis Puche Rondon Florida International University

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Dr. Luis C. Puche Rondon is a graduate of Florida International University and an alumni of the Cyber-Physical Systems Security Lab (CSL). He received his Bachelors in Computer Science in 2016, and a Masters in Cybersecurity in 2017. Luis has ten years of work experience in Smart home integration and solutions. His research interests include the security of smart environments such as smart homes and offices.

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Daniel Ortiz Florida International University

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Computer science major focused on artificial intelligence/machine learning and interested in Natural Language Processing applications. Passionate about diversity in the tech industry!

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Monique Ross Florida International University

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Assistant Professor, Knight Foundation School of Computing and Information Sciences and STEM Transformation Institute at Florida International University, research interests include broadening participation in computing through the exploration of: 1) race, gender, and identity in the academy and industry; 2) discipline-based education research that informs pedagogical practices that garner interest and retain women and minorities in computer-related fields. She uses her scholarship to challenge the perceptions of who belong in computing.

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Mark Finlayson Florida International University

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Dr. Mark A. Finlayson is Eminent Scholar Chaired Associate Professor of Computer Science and Interim Associate Director in the Knight Foundation School of Computing and Information Sciences (KFSCIS) at Florida International University (FIU). His research intersects artificial intelligence, natural language processing, and cognitive science. He directs the FIU KFSCIS Cognition, Narrative, and Culture (Cognac) Laboratory whose members focus on advancing the science of narrative, including: understanding the relationship between cognition, narrative, and culture; developing new methods and techniques for investigating questions related to language and narrative; and endowing machines with the ability to understand and use narratives for a variety of applications. He received his Ph.D. from MIT in computer science in 2012 under the supervision of Professor Patrick H. Winston. He also holds the M.S. in Electrical Engineering from MIT (2001) and B.S. in Electrical Engineering from the University of Michigan, Ann Arbor (1998). Dr. Finlayson served as a research scientist at the MIT Computer Science and Artificial Intelligence Laboratory for 2½ years before coming to FIU. Dr. Finlayson received an NSF CAREER Award in 2018, an IBM Faculty Award in 2019, and was named the Edison Fellow for AI at the U.S. Patent and Trademark Office for 2019–2021. Dr. Finlayson received FIU’s University-wide Faculty Award for Excellence in Research and Creative Activities, and has received university and departmental awards for Service, Teaching, Mentoring, and Research. His work has been funded by NSF, NIH, ONR, DARPA, DHS, and IBM.

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A. Uluagac Florida International University

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Abstract

As societies rely increasingly on computers for critical functions, the importance of cybersecurity becomes ever more paramount. Even in recent months there have been attacks that halted oil production, disrupted online learning at the height of COVID, and put medical records at risk at prominent hospitals. This constant threat of privacy leaks and infrastructure disruption has led to an increase in the adoption of artificial intelligence (AI) techniques, mainly machine learning (ML), in state-of-the-art cybersecurity approaches. Oftentimes, these techniques are borrowed from other disciplines without context and devoid of the depth of understanding as to why such techniques are best suited to solve the problem at hand. This is largely due to the fact that in many ways cybersecurity curricula have failed to keep up with advances in cybersecurity research and integrating AI and ML into cybersecurity curricula is extremely difficult. To address this gap, we propose a new methodology to integrate AI and ML techniques into cybersecurity education curricula. Our methodology consists of four components: i) Analysis of Literature which aims to understand the prevalence of AI and ML in cybersecurity research, ii) Analysis of Cybersecurity Curriculum that intends to determine the materials already present in the curriculum and the possible intersection points in the curricula for the new AI material, iii) Design of Adaptable Modules that aims to design highly adaptable modules that can be directly used by cybersecurity educators where new AI material can naturally supplement/substitute for concepts or material already present in the cybersecurity curriculum, and iv) Curriculum Level Evaluation that aims to evaluate the effectiveness of the proposed methodology from both student and instructor perspectives. In this paper, we focus on the first component of our methodology - Analysis of Literature and systematically analyze over 5000 papers that were published in the top cybersecurity conferences during the last five years. Our results clearly indicate that more than 78% of the cybersecurity papers mention AI terminology. To determine the prevalence of the use of AI, we randomly selected 300 papers and performed a thorough analysis. Our results show that more than 19% of the papers implement ML techniques. These findings suggest that AI and ML techniques should be considered for future integration into cybersecurity curriculum to better align with advancements in the field.

ARIS, A., & Puche Rondon, L., & Ortiz, D., & Ross, M., & Finlayson, M., & Uluagac, A. (2022, August), Integrating Artificial Intelligence into Cybersecurity Curriculum: New Perspectives Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41761

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