The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Internet of Things attack detection using hybrid Deep Learning Model
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955
Ensemble deep learning: A review
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105151 · Cited by 2,143
Self-citationN-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018) · IEEE Pervasive Computing · doi:10.1109/mprv.2018.03367731 · Cited by 1,483
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392
Machine Learning and Deep Learning Methods for Cybersecurity
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018) · IEEE Access · doi:10.1109/access.2018.2836950 · Cited by 1,228
Distributed attack detection scheme using deep learning approach for Internet of Things
Diro, A. A., & Chilamkurti, N. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.043 · Cited by 1,053
A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting
HaddadPajouh, H., Dehghantanha, A., Khayami, R., & Choo, K.-K. R. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.03.007 · Cited by 445
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning
Azmoodeh, A., Dehghantanha, A., & Choo, K.-K. R. (2019) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2018.2809665 · Cited by 378
Thakkar, A., & Lohiya, R. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09496-0 · Cited by 366
Cited by
Cherbal, S., Zier, A., Hebal, S., Louail, L., & Annane, B. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-023-05616-2 · Cited by 152
Metadata
- Authors
- Sahu, A. K., Sharma, S., Tanveer, M., & Raja, R.
- Venue
- Computer Communications
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 176
- Pages
- 146-154
- ISSN
- 0140-3664 (ISSN-L)
- OpenAlex
- W3169330763
- Cited by
- 258
- References
- 27
- FWCI
- 19.78
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Internet of Things attack detection using hybrid Deep Learning Model. https://app.cochat.ai/papers/10.1016/j.comcom.2021.05.024
- The paper
- Sahu, A. K., Sharma, S., Tanveer, M., & Raja, R. (2021). Internet of Things attack detection using hybrid Deep Learning Model. Computer Communications. https://doi.org/10.1016/j.comcom.2021.05.024
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
