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Machine Learning Algorithms for Preventing IoT Cybersecurity Attacks

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


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.


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

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  1. Lu, Y., Xu, L.D.: Internet of Things (IoT) cybersecurity research: a review of current research topics. IEEE Internet Things J. 6(2), 2103–2115 (2019).

    CrossRef  Google Scholar 

  2. Cañedo, J., Skjellum, A.: Using machine learning to secure IoT systems. In: 2016 14th Annual Conference on Privacy, Security and Trust (PST), pp. 219–222, Auckland (2016)

    Google Scholar 

  3. Meneghello, F., Calore, M., Zucchetto, D., Polese, M., Zanella, A.: IoT: Internet of threats? A survey of practical security vulnerabilities in real IoT devices. IEEE Internet Things J. 6(5), 8182–8201 (2019).

    CrossRef  Google Scholar 

  4. Mamdouh, M., Elrukhsi, M.A.I., Khattab, A.: Securing the Internet of Things and wireless sensor networks via machine learning: a survey. In: 2018 International Conference on Computer and Applications (ICCA), pp. 215–218, Beirut (2018)

    Google Scholar 

  5. Xiao, L., Wan, X., Lu, X., Zhang, Y., Wu, D.: IoT security techniques based on machine learning: how do IoT devices use AI to enhance security? IEEE Signal Process. Mag. 35(5), 41–49 (2018).

    CrossRef  Google Scholar 

  6. Hou, S., Huang, X.; Use of machine learning in detecting network security of edge computing system. In: 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), pp. 252–256, Suzhou (2019).

  7. Roopak, M., Yun Tian, G., Chambers, J.: Deep learning models for cyber security in IoT networks. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0452–0457, Las Vegas (2019).

  8. Vishwakarma, R., Jain, A.K.: A Honeypot with machine learning based detection framework for defending IoT based botnet DDoS attacks. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1019–1024, Tirunelveli (2019)

    Google Scholar 

  9. Sharafaldin, I., Lashkari, A., Hakak, S., Ghorbani, A.: Developing realistic distributed denial of Service (DDoS) attack dataset and taxonomy. New Brunswick University, Canadian Institute of Cybersecurity (CIC).

  10. Moustafa, N.: The Bot-IoT dataset. IEEE Dataport (2019). Accessed 15 Jan 2020

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

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Correspondence to Steve Chesney .

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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.

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