A Hybrid Deep Learning Approach for Bottleneck Detection in IoT

Article Properties
  • DOI (url)
    10.1109/access.2022.3188635
  • Publication Date
    2022/01/01
  • Journal
    IEEE Access
  • Indian UGC (journal)
  • Refrences
    61
  • Fraidoon Sattari Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
  • Ashfaq Hussain Farooqi Department of Computer Science, Air University, Islamabad, Pakistan ORCID (unauthenticated)
  • Zakria Qadir School of Computing Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia ORCID (unauthenticated)
  • Basit Raza Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan ORCID (unauthenticated)
  • Hadi Nazari Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan ORCID (unauthenticated)
  • Muhannad Almutiry Department of Electrical Engineering, Northern Border University, Arar, Saudi Arabia ORCID (unauthenticated)
Cite
Sattari, Fraidoon, et al. “A Hybrid Deep Learning Approach for Bottleneck Detection in IoT”. IEEE Access, vol. 10, 2022, pp. 77039-53, https://doi.org/10.1109/access.2022.3188635.
Sattari, F., Farooqi, A. H., Qadir, Z., Raza, B., Nazari, H., & Almutiry, M. (2022). A Hybrid Deep Learning Approach for Bottleneck Detection in IoT. IEEE Access, 10, 77039-77053. https://doi.org/10.1109/access.2022.3188635
Sattari, Fraidoon, Ashfaq Hussain Farooqi, Zakria Qadir, Basit Raza, Hadi Nazari, and Muhannad Almutiry. “A Hybrid Deep Learning Approach for Bottleneck Detection in IoT”. IEEE Access 10 (2022): 77039-53. https://doi.org/10.1109/access.2022.3188635.
Sattari F, Farooqi AH, Qadir Z, Raza B, Nazari H, Almutiry M. A Hybrid Deep Learning Approach for Bottleneck Detection in IoT. IEEE Access. 2022;10:77039-53.
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Refrences
Title Journal Journal Categories Citations Publication Date
Intrusion detection system for IoT botnet attacks using deep learning 2021
An analysis of deep neural network models for practical applications 2016
10.1109/TII.2019.2937079
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