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Authentic Learning on Machine Learning for Cybersecurity

Authors:

Dan Lo,

Hossain Shahriar,

Kai Qian,

Michael Whitman,

Fan Wu,

Cassandra ThomasAuthors Info & Claims

SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2

Page 1299

https://doi.org/10.1145/3545947.3576245

Published: 06 March 2023 Publication History

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Abstract

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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MP4 File (SIGCSE23-V2pp0804-.mp4)

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 (ML4CybrS) 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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Index Terms

Authentic Learning on Machine Learning for Cybersecurity
1. Security and privacy
  1. Security services
    1. Pseudonymity, anonymity and untraceability

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Published In

SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2

March 2023

1481 pages

ISBN:9781450394338

DOI:10.1145/3545947

General Chairs:
Maureen Doyle
Northern Kentucky University, USA
,
Ben Stephenson
University of Calgary, Canada
,
Program Chairs:
Brian Dorn
University of Nebraska at Omaha, USA
,
Leen-Kiat Soh
University of Nebraska-Lincoln, USA
,
Lina Battestilli
North Carolina State University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 March 2023

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NSF (National Science Foundation)

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SIGCSE 2023

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SIGCSE

SIGCSE 2023: The 54th ACM Technical Symposium on Computer Science Education

March 15 - 18, 2023

Toronto ON, Canada

Acceptance Rates

Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

Upcoming Conference

SIGCSE TS 2025

Sponsor:
sigcse

The 56th ACM Technical Symposium on Computer Science Education

February 26 - March 1, 2025

Pittsburgh , PA , USA

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Index Terms

Recommendations

Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware

Closing the Experiential Learning Loops Using Learning Analytics Cycle: Towards Authentic Experience Sharing for Vocabulary Learning

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