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Authentic Learning on Machine Learning for Cybersecurity
 

Authentic Learning on Machine Learning for Cybersecurity

Dan Lo, Hossain Shahriar, Kai Qian, Michael Whitman, Fan Wu Cassandra Thomas
Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2, Vol.2, p.1299
ACM Conferences
ACM Technical Symposium on Computer Science Education (SIGCSE 2023) , 54th (Toronto, Ontario, Canada, 03/15/2023–03/18/2023)
2023
: https://doi.org/10.1145/3545947.3576245
: WOS:001267734200041
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authentic learning Cybersecurity Machine Learning
The primary goal of the authentic learning approach is to engage and motivate students in learning real world problem solving. We report our experience in developing k-nearest neighbor (KNN) classification for anomaly user behavior detection, one of the authentic machine learning for cybersecurity (ML4Cybr) learning modules based on 10 cybersecurity (CybrS) cases with machine learning (ML) solutions. All portable labs are made available on Google CoLab. So students can access and practice these hands-on labs anywhere and anytime without software installation and configuration which will engage students in learning concepts immediately and getting more experience for hands-on problem solving skills.

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Authentic Learning on Machine Learning for Cybersecurity
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