Authentic Learning of Machine Learning to Ransomware Detection and Prevention
Md Jobair Hossain Faruk, Mohammad Masum, Hossain Shahriar, Kai Guo Qian, Dan Chia-Tien Lo · 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) · 2022
The primary goal of the authentic learning provides students with an engaging and motivating learning environment for students with hands-on experiences in solving real-world security problems. Each learning topic consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. With an authentic learning approach, we design and develop portable labware on Google CoLab for ML for ransomware detection and prevention so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem-solving skills.