Abstract: Because the World Wide Web plays a significant role on the Internet, its security is a core issue that must be addressed in cybersecurity degree programs to adequately prepare students for leadership in the industry. To teach a “Web Application Security” course, a good exercise platform that can cover the context of Web application is crucial to the learning outcomes. Unfortunately, existing platforms cannot satisfy both cost and efficiency requirements. In this paper, a cost-effective, quick, and accurate face recognition Web Application, ESP32-CAM, is introduced to the course with minor adaptions to provide a richer learning experience to students. Based on the students’ evaluation, this Internet of Things hardware-based Artificial Intelligence Web application can provide more hands-on exercises in the class and further inspire the students’ learning interests on a matured technique such as Web applications. Furthermore, through this platform students can apply the cutting-edge technologies in their class projects or capstone project, e.g., “transfer learning” to extend the face recognition to emotion recognition or generative adversarial network to fool the Artificial Intelligence model.
Download this article: CPPJ - V1 N1 Page 44.pdf
Recommended Citation: Li, Z., Chou, E., McAllister, C., (2022). An IoT Based New Platform for Teaching Web Application Security. Cybersecurity Pedagogy and Practice Journal1(1) pp 44-53. http://CPPJ.org/2022-1/ ISSN#: 2832-1006. A preliminary version appears in The Proceedings of EDSIGCON 2021