Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware: Neural Network Algorithms for Network Denial of Service (DOS) Detection-Reference-Cited by-同舟云学术

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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

Published:2022-12-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Big Data (Big Data)
language:
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Author:

Hossain Faruk Md Jobair¹,Shahriar Hossain²,Qian Kai³,Lo Dan³,Whitman Michael⁴,Cuzzocrea Alfredo⁵,Wu Fan⁶,Clincy Victor³

Affiliation:

1. Kennesaw State University,Department of Software Engineering and Game Development,USA

2. Kennesaw State University,Department of Information Technology,USA

3. Kennesaw State University,Institute for Cyber Workforce Development,USA

4. Kennesaw State University,Department Computer Science,USA

5. University of Calabria,iDEA Lab,Rende,Italy

6. Tuskegee University,Department of Computer Science,USA

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020792.pdf?arnumber=10020792

Reference8 articles.

1. Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection

2. Systematic literature review and taxonomy for DDoS attack detection and prediction;selamat;Int J Dig Ent Techn,2019

3. New technologies, new pedagogies: Mobile technologies and new ways of teaching and learning;herrington;ASCILITE 2008 - Australas Soc Comput Learn Tert Educ,2008

4. Authentic learning of mobile security with case studies

5. A comprehensive cybersecurity learning platform for elementary education

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