OPSMOTE-ML: an optimized SMOTE with machine learning models for selective forwarding attack detection in low power and lossy networks of internet of things-Reference-Cited by-同舟云学术

OPSMOTE-ML: an optimized SMOTE with machine learning models for selective forwarding attack detection in low power and lossy networks of internet of things

Published:2024-06-08 Issue: Volume: Page:
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Al-Amiedy Taief Alaa,Anbar Mohammed,Belaton Bahari

Funder

This research was supported by a Research University (RU) Grant, Universiti Sains Malaysia

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10586-024-04598-x.pdf

Reference61 articles.

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2. Bhuiyan, M.N., Rahman, M.M., Billah, M.M., Saha, D.: Internet of Things (IoT): a review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities. IEEE Internet Things J. 8, 10474–10498 (2021). https://doi.org/10.1109/JIOT.2021.3062630

3. Hintaw, A.J., Manickam, S., Karuppayah, S., Aladaileh, M.A., Aboalmaaly, M.F., Laghari, S.U.A.: A robust security scheme based on enhanced symmetric algorithm for MQTT in the Internet of Things. IEEE Access. 11, 43019–43040 (2023). https://doi.org/10.1109/ACCESS.2023.3267718

4. Al-Amiedy, T.A., Anbar, M., Belaton, B., Kabla, A.H.H., Hasbullah, I.H., Alashhab, Z.R.: A systematic literature review on machine and deep learning approaches for detecting attacks in RPL-based 6LoWPAN of Internet of Things. Sensors. 22, 3400 (2022). https://doi.org/10.3390/s22093400

5. Shukla, P.: ML-IDS: s machine learning approach to detect wormhole attacks in Internet of Things. In: Intelligent Systems Conference, IntelliSys 2017. pp. 234–240. IEEE (2018)

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