On the Performance of Machine Learning Models for Anomaly-Based Intelligent Intrusion Detection Systems for the Internet of Things

Abdelmoumin, Ghada; Rawat, Danda B.; Rahman, Abdul Malek Abdul

doi:10.1109/JIOT.2021.3103829

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On the Performance of Machine Learning Models for Anomaly-Based Intelligent Intrusion Detection Systems for the Internet of Things

Author(s): Ghada Abdelmoumin ¹ , Danda B. Rawat ¹ , Abdul Rahman ²

Publication date (Print): March 15 2022

Journal: IEEE Internet of Things Journal

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

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Most cited references 34

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Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset

Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova … (2019)

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A Detailed Investigation and Analysis of using Machine Learning Techniques for Intrusion Detection

Preeti Mishra, Vijay Varadharajan, Uday Tupakula … (2018)

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TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems

Abdullah Alsaedi, Nour Moustafa, Zahir Tari … (2020)

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Author and article information

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Ghada Abdelmoumin: (View ORCID Profile)

Danda B. Rawat: (View ORCID Profile)

Journal

Title: IEEE Internet of Things Journal

Abbreviated Title: IEEE Internet Things J.

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

ISSN (Electronic): 2327-4662

ISSN (Electronic): 2372-2541

Publication date Created: March 15 2022

Publication date (Print): March 15 2022

Volume: 9

Issue: 6

Pages: 4280-4290

Affiliations

[1 ]Department of Electrical Engineering and Computer Science, Howard University, Washington, DC, USA

[2 ]Microsoft Corp, Reston, VA, USA

Article

DOI: 10.1109/JIOT.2021.3103829

SO-VID: 8a974288-4a1f-43f6-ba25-0fd43cfb9f21

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https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html

https://doi.org/10.15223/policy-029

https://doi.org/10.15223/policy-037

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On the Performance of Machine Learning Models for Anomaly-Based Intelligent Intrusion Detection Systems for the Internet of Things

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Exponential Random Graph Models

Most cited references 34

Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset

A Detailed Investigation and Analysis of using Machine Learning Techniques for Intrusion Detection

TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems

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Article

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