The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Hybrid Intrusion Detection System for Internet of Things (IoT)
Abstract
Internet of things (IoT) is a promising solution to connect and access every device through internet. Every day the device count increases with large diversity in shape, size, usage and complexity. Since IoT drive the world and changes people lives with its wide range of services and applications. However, IoT provides numerous services through applications, it faces severe security issues and vulnerable to attacks such as sinkhole attack, eaves dropping, denial of service attacks, etc., Intrusion detection system is used to detect such attacks when the network security is breached. This research work proposed an intrusion detection system for IoT network and detect different types of attacks based on hybrid convolutional neural network model. Proposed model is suitable for wide range of IoT applications. Proposed research work is validated and compared with conventional machine learning and deep learning model. Experimental result demonstrate that proposed hybrid model is more sensitive to attacks in the IoT network.
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Citations
Cites
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
Cited by
Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L. F., & Abdulkadir, S. J. (2022) · Electronics · doi:10.3390/electronics11020198 · Cited by 361
GNN-IDS: Graph Neural Network based Intrusion Detection System
Sun, Z., Teixeira, A. M. H., & Toor, S. (2024) · Proceedings of the 19th International Conference on Availability, Reliability and Security · doi:10.1145/3664476.3664515 · Cited by 80
A bibliometric method for analyzing IoT hacking solutions in network services
KOMBATE, Y., HOUNGUE, P., & OUYA, S. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.07.020 · Cited by 7
Metadata
- Authors
- Smys, D. S., Basar, D. A., & Wang, D. H.
- Venue
- Journal of ISMAC
- Publisher
- Inventive Research Organization
- Published
- 2020-09-30
- Volume
- 2
- Issue
- 4
- Pages
- 190-199
- ISSN
- 2582-1369 (ISSN-L)
- OpenAlex
- W3090111074
- Cited by
- 299
- References
- 1
- FWCI
- 23.22
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Hybrid Intrusion Detection System for Internet of Things (IoT). https://app.cochat.ai/papers/10.36548/jismac.2020.4.002
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- Smys, D. S., Basar, D. A., & Wang, D. H. (2020). Hybrid Intrusion Detection System for Internet of Things (IoT). Journal of ISMAC. https://doi.org/10.36548/jismac.2020.4.002
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.
