An efficient deep recurrent neural network for detection of cyberattacks in realistic IoT environment-Reference-Cited by-同舟云学术

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An efficient deep recurrent neural network for detection of cyberattacks in realistic IoT environment

Published:2024-03-09 Issue:10 Volume:80 Page:13557-13575
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Abbas Sidra,Alsubai Shtwai,Ojo Stephen,Sampedro Gabriel Avelino,Almadhor Ahmad,Hejaili Abdullah Al,Bouazzi Imen

Funder

Deanship of Scientific Research, King Khalid University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-05993-2.pdf

Reference24 articles.

1. Zhang H, Li B, Karimi M et al (2023) Recent advancements in IoT implementation for environmental, safety, and production monitoring in underground mines. IEEE Internet Things J 10(16):14507–14526. https://doi.org/10.1109/JIOT.2023.3267828

2. Prathyusha MR, Bhowmik B (2023) IoT evolution and recent advancements. In: 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), pp 1725–1730. https://doi.org/10.1109/ICACCS57279.2023.10112761

3. Rahman MA, Asyhari AT, Wen OW et al (2021) Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection. Multimed Tools Appl 1–19

4. Thakkar A, Lohiya R (2021) A review on machine learning and deep learning perspectives of ids for IoT: recent updates, security issues, and challenges. Arch Comput Methods Eng 28:3211–3243

5. Srinivas J, Das AK, Kumar N (2019) Government regulations in cyber security: framework, standards and recommendations. Future Gener Comput Syst 92:178–188

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1. Efficient and Generalized Image-Based CNN Algorithm for Multi-Class Malware Detection;IEEE Access;2024

2. Optimizing Smart Home Intrusion Detection With Harmony-Enhanced Extra Trees;IEEE Access;2024

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