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Distributed attack detection scheme using deep learning approach for Internet of Things
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Cites
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Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
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
Network Intrusion Detection for IoT Security Based on Learning Techniques
Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2896380 · Cited by 940
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. M. A. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100059 · Cited by 841
Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756
Metadata
- Authors
- Diro, A. A., & Chilamkurti, N.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 82
- Pages
- 761-768
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2752291283
- Cited by
- 1,053
- References
- 3
- FWCI
- 44.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- La Trobe University
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- The paper
- Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.08.043
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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.
