The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Towards a deep learning-driven intrusion detection approach for Internet of Things
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Citations
Cites
Deep Learning-Based Intrusion Detection for IoT Networks
Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., & Robles-Kelly, A. (2019) · 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC) · doi:10.1109/prdc47002.2019.00056 · Cited by 269
Self-citation
Cited by
Design and Development of RNN Anomaly Detection Model for IoT Networks
Ullah, I., & Mahmoud, Q. H. (2022) · IEEE Access · doi:10.1109/access.2022.3176317 · Cited by 270
Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0
Ferrag, M. A., Shu, L., Djallel, H., & Choo, K.-K. R. (2021) · Electronics · doi:10.3390/electronics10111257 · Cited by 253
Deepfakes: current and future trends
Gambín, Á. F., Yazidi, A., Vasilakos, A., Haugerud, H., & Djenouri, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10679-x · Cited by 145
An Adaptive Intrusion Detection System in the Internet of Medical Things Using Fuzzy-Based Learning
Alalhareth, M., & Hong, S.-C. (2023) · Sensors · doi:10.3390/s23229247 · Cited by 52
A Systematic Review of Data-Driven Attack Detection Trends in IoT
Haque, S., El-Moussa, F., Komninos, N., & Muttukrishnan, R. (2023) · Sensors · doi:10.3390/s23167191 · Cited by 38
Metadata
- Authors
- Ge, M., Syed, N. F., Fu, X., Baig, Z., & Robles-Kelly, A.
- Venue
- Computer Networks
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 186
- Pages
- 107784
- ISSN
- 1389-1286 (ISSN-L)
- OpenAlex
- W3121022046
- Cited by
- 190
- References
- 1
- FWCI
- 15.62
- Type
- journal article
- Language
- en
- Open access
- green
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- Ge, M., Syed, N. F., Fu, X., Baig, Z., & Robles-Kelly, A. (2021). Towards a deep learning-driven intrusion detection approach for Internet of Things. Computer Networks. https://doi.org/10.1016/j.comnet.2020.107784
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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.
