The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT
Open accessAbstract
With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IoT Sentinel, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IoT Sentinel is effective in identifying device types and has minimal performance overhead.
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Cited by
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
DÏoT: A Federated Self-learning Anomaly Detection System for IoT
Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00080 · Cited by 663
Self-citationMachine learning based solutions for security of Internet of Things (IoT): A survey
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102630 · Cited by 470
Iqbal, W., Abbas, H., Daneshmand, M., Rauf, B., & Bangash, Y. A. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2997651 · Cited by 356
A survey on application of machine learning for Internet of Things
Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N., & Qin, J. (2018) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-018-0834-5 · Cited by 293
Metadata
- Authors
- Miettinen, M., Marchal, S., Hafeez, I., Asokan, N., Sadeghi, A.-R., & Tarkoma, S.
- Venue
- 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- 2177-2184
- OpenAlex
- W2574448563
- Cited by
- 666
- References
- 24
- FWCI
- 48.22
- Type
- conference paper
- Language
- en
- Open access
- green
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- Miettinen, M., Marchal, S., Hafeez, I., Asokan, N., Sadeghi, A.-R., & Tarkoma, S. (2017). IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). https://doi.org/10.1109/icdcs.2017.283
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