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Proceedings of SAI Intelligent Systems Conference

IntelliSys 2020: Intelligent Systems and Applications pp 679–686Cite as

Machine Learning Algorithms for Preventing IoT Cybersecurity Attacks

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1252)

Abstract

The goal of this paper is to understand the effectiveness of machine learning (ML) algorithms in combatting IoT-related cyber-attacks, with a focus on Denial of Service (DoS) attacks. This paper also explores the overall vulnerabilities of IoT devices to cyber-attacks, and it investigates other datasets that can be used for IoT cyber-defense analysis, using ML techniques. Finally, this paper presents an evaluation of the CICDoS2019 dataset, using the Logistic Regression (LR) algorithm. With this algorithm, a prediction accuracy of 0.997 was achieved.

Keywords

  • Cybersecurity
  • Internet of Things
  • Machine learning (ML)
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning (RL)
  • Logistic Regression (LR)
  • DDoS
  • Botnet

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References

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Acknowledgments

This research is based upon the work supported by Cisco Systems, Inc.

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Authors and Affiliations

  1. Applied Science and Technology, Department of Computer Science, N.C. A&T State University, Greensboro, NC, 27411, USA

    Steve Chesney, Kaushik Roy & Sajad Khorsandroo

Authors
  1. Steve Chesney
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  2. Kaushik Roy
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  3. Sajad Khorsandroo
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Corresponding author

Correspondence to Steve Chesney .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Prof. Dr. Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Prof. Rahul Bhatia

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Cite this paper

Chesney, S., Roy, K., Khorsandroo, S. (2021). Machine Learning Algorithms for Preventing IoT Cybersecurity Attacks. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_53

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