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SmartIDS: A Comparative Study of Intelligent Intrusion Detection Systems for Internet of Things

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Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1 (FTC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 358))

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Abstract

Traditional intrusion detection systems (IDSs) are not scalable and efficient in detecting intrusions in IoT systems; hence, protecting them against cyber-attacks. The need to secure the Internet of Things (IoT) platforms deployable at a large scale to build smart systems gave rise to a new class of intelligent and scalable IDSs. Intelligent IDSs, which employ machine-learning (ML) or deep learning (DL) methods, have shown promising results in detecting intrusions with high accuracy and better detection rates than traditional IDSs that suffer from scalability, low detection rates, and inefficiency issues. This paper presents a comparative analysis of a selected set of intelligent IDSs using the Microsoft Azure ML Studio (AML-S) platform and datasets containing malicious and benign IoT network traffic.

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Acknowledgment

This work is supported in part by US National Science Foundation under the grant number 1828811 and CNS/SaTC 2039583, and by the US Department of Homeland Security (DHS) under grant award number, 2017-ST-062–000003. However, any opinion, finding, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

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Authors and Affiliations

  1. Howard University, Washington, DC, 20059, USA

    Ghada Abdelmoumin & Danda B. Rawat

Authors
  1. Ghada Abdelmoumin
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  2. Danda B. Rawat
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Correspondence to Ghada Abdelmoumin .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Abdelmoumin, G., Rawat, D.B. (2022). SmartIDS: A Comparative Study of Intelligent Intrusion Detection Systems for Internet of Things. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1. FTC 2021. Lecture Notes in Networks and Systems, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-030-89906-6_28

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