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XGBoost Tuned by Hybridized SCA Metaheuristics for Intrusion Detection in Healthcare 4.0 IoT Systems

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Evolutionary Artificial Intelligence (ICEASSM 2017)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Internet of Things (IoT) system advancements have facilitated their extensive incorporation into our daily lives. In particular, real-time monitoring systems are highly valuable in domains like healthcare, where prompt actions can significantly impact outcomes. However, despite the widespread adoption of IoT, a crucial obstacle hinders its broader integration. For IoT to support sustainable healthcare, it must deliver well-organized healthcare services to the population while ensuring minimal harm to the environment. Security emerges as a pivotal aspect in maintaining the sustainability of IoT systems, necessitating the timely detection and remediation of security issues. This study addresses security challenges directly by employing an XGBoost model, tuned by a hybridized sine cosine (SCA) metaheuristics algorithm, to identify security vulnerabilities in applied IoT appliances in healthcare 4.0.

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Author information

Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11010, Belgrade, Serbia

    Miodrag Zivkovic, Luka Jovanovic, Nebojsa Bacanin, Aleksandar Petrovic, Nikola Savanovic & Milos Dobrojevic

Authors
  1. Miodrag Zivkovic
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  2. Luka Jovanovic
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  3. Nebojsa Bacanin
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  4. Aleksandar Petrovic
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  5. Nikola Savanovic
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  6. Milos Dobrojevic
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Corresponding author

Correspondence to Nebojsa Bacanin .

Editor information

Editors and Affiliations

  1. Faculty of Innovation and Technology, Taylor’s University, Subang Jaya, Selangor, Malaysia

    David Asirvatham

  2. University of Southeast Norway, Notodden, Norway

    Francisco M. Gonzalez-Longatt

  3. Gdansk University of Technology, Gdańsk, Poland

    Przemyslaw Falkowski-Gilski

  4. Professor of Computer Engineering, Papua New Guinea University of Technology, Lae, Papua New Guinea

    R. Kanthavel

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zivkovic, M., Jovanovic, L., Bacanin, N., Petrovic, A., Savanovic, N., Dobrojevic, M. (2024). XGBoost Tuned by Hybridized SCA Metaheuristics for Intrusion Detection in Healthcare 4.0 IoT Systems. In: Asirvatham, D., Gonzalez-Longatt, F.M., Falkowski-Gilski, P., Kanthavel, R. (eds) Evolutionary Artificial Intelligence. ICEASSM 2017. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8438-1_1

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