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Authentic Learning of Machine Learning to Ransomware Detection and Prevention
Conference proceeding   Peer reviewed

Authentic Learning of Machine Learning to Ransomware Detection and Prevention

Md Jobair Hossain Faruk, Mohammad Masum, Hossain Shahriar, Kai Qian and Dan Lo
2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), pp.442-443
Annual Computers, Software, and Applications Conference (COMPSAC), 46th (Los Alamitos, California, USA, 06/27/2022–07/01/2022)
08/2022
DOI: https://doi.org/10.1109/COMPSAC54236.2022.00081
Web of Science ID: WOS:000855983300073

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Abstract

algorithm authentic learning Internet ransom ware detection and prevention Ransomware Algorithms Cybersecurity Higher Education Learning Motivation Machine Learning Software Computer Security
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.

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