Curriculum for Hands-on Artificial Intelligence Cybersecurity
Gordon W. Romney, James Guymon, Miles David Romney, Dennis A. Carlson · 2019
Interest and awareness of Artificial Intelligence (AI) grows at such a rate that academia and higher education struggle keeping up with the accelerating demand of industry. It is forecast that 75% of enterprise applications will use AI, Machine Learning or Deep Learning technology by 2021, yet university programs commonly place the burden on students to obtain their data science educations through elective coursework spanning multiple disparate departments. Data science requires specific mathematics preparation especially for cybersecurity students whose programs have reduced the requirements for advance mathematics to a bare minimum. To facilitate curricula preparation, and hands-on usage of AI tools, a notebook (“A Trellis For Novice AI Practitioners”) was prepared in the R programming language as a first step in introducing computer science and cybersecurity students to the concepts and capabilities of AI. A focus on an intrusion detection data set to mitigate nine common cyber vulnerabilities is used in this analysis. Trellis bridges the theoretical and practical chasm for students by building an ANN network intrusion predictive model from scratch. It serves as a template but also encourages heavy contextual modification, and may be relied upon in the beginning stages of a cybersecurity practitioner's data science activities on a wide variety of data sets in all areas of the discipline.