Integrating Data Science into Cybersecurity Education via Hands-on Labs
Temilola Aderibigbe (Florida A&M University)
Co-author: Hongmei Chi (Florida A&M University)
This poster focuses on various algorithms and data analytics methods to study the problems of cyber security, including but not limited to, intrusion detection, abnormal detection, insider threat, mobile health, etc. Most real-world cybersecurity problems have to deal with huge dataset. The importance of cybersecurity motivates the need to identify and understand appropriate data analytics algorithms that account for better solution for those complex problems. The goal of this poster is to design and implement hands-on labs integrating into security courses. We are focusing on how to integrate those labs into our cyber security courses. In addition, case studies and hands-on labs are discussed in our teaching practice.