Security threats are evolving and getting more hidden and complicated. Detecting malicious security threats and attacks has become a huge burden to our cyberspace. We should apply proactive prevention and early detection of security vulnerabilities and threats rather than patching security holes afterward. To analyze the huge amount of data to find out suspicious behaviors, threat patterns, and vulnerabilities and to predict and prevent future cybersecurity threats is a challenge. Machine Learning (ML) is a powerful instrument to take up such challenges. Authentic learning has gained popularity in recent years to teach cybersecurity topics. The authentic learning approach creates an engaging and motivating learning environment that encourages all students in learning emerging technologies with hands-on laboratory practice on real-world topics, where each topic consists of a series of progressive sub-labs: a pre-lab, hands-on lab activity, and a student add-on post-lab. Many schools offer ML courses and cybersecurity courses in their computing curriculum; however, authentic learning-based ML into cybersecurity curriculum is not presently commonplace. There is a scarcity of open-source portable hands-on labware for the authentic learning of ML in Cybersecurity (MLC). Challenges in offering authentic learning-based MLC resources commonly include high costs of infrastructure, configuration difficulties of open source applications, a shortage of qualified faculty and technical staff, and the time constraints associated with developing open-source materials. To overcome these difficulties, this project proposes the development of a cyber workforce using authentic learning of MLC topics through the set of real-world cybersecurity learning modules with a pre-lab, lab activity, and post-add-on lab learning cycle. The proposed portable labware will be designed, developed, and deployed on the open-source Google CoLaboratory (CoLab) environment where learners can access and practice all labs interactively with browsers anywhere and anytime without tedious installation and configuration. Also, the proposed hands-on lab modules will support a wide audience to effectively learn the subjects and result in more efficient student learning and engagement. This project will help to enhance the cybersecurity curricula across computing disciplines integrated with data science, and engage students' active learning and problem-solving capability.
