Designing a High School Course on Machine Learning for Cyberthreat Analytics
Kossi Bissadu, Gahangir Hossain · 2024
Introducing secure computing through the integration of Machine Learning (ML) applications at the early education stage, particularly within high school, presents both benefits and challenges. This paper conceptualizes the imperative, hurdles, and the blueprint for implementing a hands-on machine learning curriculum within school-level computing education. The convergence of ML with secure computing necessitates the prior inclusion of lessons encompassing precomputing, ethics, and privacy to prepare students effectively. Consequently, this integration is recommended for high school education (9th to 12th grade) due to maturity and comprehension levels of students at this stage. Considering the foundational knowledge and backgrounds of school students, our proposal advocates the adoption of basic and user-friendly ML tool, such as Weka, tailored to the school environment. This article delves into several facets of the Weka tool, elucidating its relevance in the context of research directions and methodologies.