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1. Integrity, Confidentiality, and Equity: Using Inquiry-Based Labs to help students understand AI and Cybersecurity

   https://doi.org/10.32727/8.2023.34  

   Alexander, Richard; Ma, Liran; Dou, Ze-Li; Cai, Zhipeng; Huang, Yan (October 2023, Journal of Cybersecurity Education Research and Practice)

   Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students. 

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2. Integrity, Confidentiality, and Equity: Using Inquiry-Based Labs to help students understand AI and Cybersecurity

   Alexander, Richard C; Ma, Liran; Dou, Ze-Li; Cai, Zhipeng; Huang, Yan (November 2023, The Journal of Cybersecurity Education Research and Practice (JCERP) i)

   Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students. 

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3. Cybersecurity Education with Generative AI: Creating Interactive Labs from Microelectronic Fundamentals to IoT Security Exploitation

   Badal, K; Yuan, X; Fu, H; Hanna, D; Gorski, J (November 2025, Journal of the Colloquium for Information System Security Education)

   Creating engaging cybersecurity education materials typically requires months of development time and specialized expertise. This paper describes how we used generative AI to address this challenge. We utilized Claude AI to generate a complete interactive platform that teaches students basic microelectronics through IoT hacking. Through iterative prompting, we generated more than 15,000 lines of functional code, including interactive visualizations, Python security tools, and gamified quizzes with real-time leaderboards. The curriculum guides students through the evolution of computing—from vacuum tubes to modern IoT devices—then helps them apply this foundation to discover real vulnerabilities. We implemented this platform at a GenCyber summer camp with 40 participants, where students identified actual security issues in AmpliPi audio systems—opensource network audio devices designed for multi-room audio distribution—including password weaknesses and denial of service flaws. The entire development process took only three weeks instead of the typical several months. The AI produced quality educational content, although we reviewed everything for technical accuracy and ethical considerations. During the camp, students remained engaged through competitive elements and hands-on labs, learning both theoretical concepts and practical skills. The students used AI-generated tools, including working implementations of SlowLoris and dictionary attacks, to test real systems. Our experience demonstrates that generative AI can efficiently create effective cybersecurity education materials that remain technically current. All materials are publicly available on GitHub for educational use. This approach could help educators stay on track with the rapidly evolving technology despite traditional curriculum development constraints. 

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4. Learning database security with hands-on mobile labs

   https://doi.org/10.1109/FIE.2017.8190716  

   Qian, Kai; Lo, Dan; Shahriar, Hossain; Li, Lei; Wu, Fan; Bhattacharya, Prabir (December 2017, 2017 IEEE Frontiers in Education Conference (FIE))

   As mobile computing is becoming more and more popular, the security threats to mobile applications are simultaneously increasing explosively. Most malicious activities hack the user’s private information, such as contact and location information, hijack the user’s transactions and communications, and exploit the confidential enterprise data stored in mobile databases or in cache on mobile devices. Database security is one of the most important security areas to be addressed. Many schools are integrating database security topics into database and cybersecurity education. This paper addresses the needs for pedagogical learning materials for database security education and the challenges of building database security capacity through effective, engaging, and investigative learning approaches, through transferrable and integratable mobile-based learning modules with hands-on companion labs based on the OWASP recommendations, such as input validation, data encryption, data sharing, auditing, and others. The primary goal of this learning approach is to create a motivating learning environment that encourages and engages all students in database security concepts and practices learning. The preliminary feedback from students was positive. Students gained hands-on real world learning experiences on Mobile Database Security (MDS) with Android mobile devices, which also greatly promoted students’ self-efficacy and confidence in their mobile security learning. 

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5. Proving confidentiality in a file system using DISKSEC

   Ileri, Atalay; Chajed, Tej; Chlipala, Adam; Kaashoek, Frans; Zeldovich, Nickolai (October 2018, Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI))

   SFSCQ is the first file system with a machine-checked proof of security. To develop, specify, and prove SFSCQ, this paper introduces DiskSec, a novel approach for reasoning about confidentiality of storage systems, such as a file system. DiskSec addresses the challenge of specifying confidentiality using the notion of _data noninterference_ to find a middle ground between strong and precise information-flow-control guarantees and the weaker but more practical discretionary access control. DiskSec factors out reasoning about confidentiality from other properties (such as functional correctness) using a notion of _sealed blocks_. Sealed blocks enforce that the file system treats confidential file blocks as opaque in the bulk of the code, greatly reducing the effort of proving data noninterference. An evaluation of SFSCQ shows that its theorems preclude security bugs that have been found in real file systems, that DiskSec imposes little performance overhead, and that SFSCQ's incremental development effort, on top of DiskSec and DFSCQ, on which it is based, is moderate. 

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