A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem
- DOI
- 10.1016/j.asej.2024.102777
- Published
- 2024-07
- Container
- Ain Shams Engineering Journal
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.asej.2024.102777,
title = {A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem},
author = {Ahsan Nazir and Jingsha He and Nafei Zhu and Saima Siraj Qureshi and Siraj Uddin Qureshi and Faheem Ullah and Ahsan Wajahat and Muhammad Salman Pathan},
year = {2024},
journal = {Ain Shams Engineering Journal},
doi = {10.1016/j.asej.2024.102777},
url = {https://doi.org/10.1016/j.asej.2024.102777}
}RIS
TY - JOUR TI - A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem AU - Ahsan Nazir AU - Jingsha He AU - Nafei Zhu AU - Saima Siraj Qureshi AU - Siraj Uddin Qureshi AU - Faheem Ullah AU - Ahsan Wajahat AU - Muhammad Salman Pathan PY - 2024 JO - Ain Shams Engineering Journal DO - 10.1016/j.asej.2024.102777 UR - https://doi.org/10.1016/j.asej.2024.102777 ER -
APA
Nazir, A., He, J., Zhu, N., Qureshi, S. S., Qureshi, S. U., Ullah, F., Wajahat, A., & Pathan, M. S. (2024). A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2024.102777
Source records
- crossref · retrieved 2026-09-25T20:31:39.052Z