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Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware: Neural Network Algorithms for Network Denial of Service (DOS) Detection
 

Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware: Neural Network Algorithms for Network Denial of Service (DOS) Detection

Md Jobair Hossain Faruk, Hossain Shahriar, Kai Qian, Dan Lo, Michael Whitman, Alfredo Cuzzocrea, Fan Wu Victor Clincy
2022 IEEE International Conference on Big Data (Big Data), pp.5715-5720
International Conference on Big Data (Big Data) (Osaka, Japan, 12/17/2022–12/20/2022)
01/2023
: https://doi.org/10.1109/BigData55660.2022.10020792
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algorithm authentic learning Data models denial of services (DOS) Internet Machine learning algorithms Military standards neural network Neural networks Cybersecurity Higher Education Learning Motivation Big Data Education Machine Learning
The primary goal of the authentic learning approach is to engage and motivate students in a learning environment that encourages all students in learning. This approach provides students with hands-on experiences in solving real-world security problems. We designed and developed ten learning modules based on 10 cybersecurity cases with different ML solutions. Each learning module consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. All portable labs are made available on Google CoLab for ML to cybersecurity so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will engage students in learning concepts and getting more experience for hands-on problem-solving skills. In this paper, we adopt Neural Network Algorithms for Network Denial of Service (DOS) Detection where we apply the KDDCup 1999 datasets contain a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment. Our primary goal of this lab is to show whether a link is a malicious or safe connection. Our demonstration shows an achieved accuracy of 99.89%.
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