The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
An Ensemble of Deep Recurrent Neural Networks for Detecting IoT Cyber Attacks Using Network Traffic
Abstract
Internet-of-Things (IoT) devices and systems will be increasingly targeted by cybercriminals (including nation state-sponsored or affiliated threat actors) as they become an integral part of our connected society and ecosystem. However, the challenges in securing these devices and systems are compounded by the scale and diversity of deployment, the fast-paced cyber threat landscape, and many other factors. Thus, in this article, we design an approach using advanced deep learning to detect cyber attacks against IoT systems. Specifically, our approach integrates a set of long short-term memory (LSTM) modules into an ensemble of detectors. These modules are then merged using a decision tree to arrive at an aggregated output at the final stage. We evaluate the effectiveness of our approach using a real-world data set of Modbus network traffic and obtain an accuracy rate of over 99% in the detection of cyber attacks against IoT devices.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Accurate modeling of modbus/TCP for intrusion detection in SCADA systems
Goldenberg, N., & Wool, A. (2013) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2013.05.001 · Cited by 323 · cited 3×
“In another study, Goldenberg and Wool [15] modeled the behavior of the Modbus protocol to detect intrusions using deterministic finite automaton (DFA).”
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Cited by
A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480
Self-citationFederated-Learning-Based Anomaly Detection for IoT Security Attacks
Mothukuri, V., Khare, P., Parizi, R. M., Pouriyeh, S., Dehghantanha, A., & Srivastava, G. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3077803 · Cited by 722
Self-citationAbdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L. F., & Abdulkadir, S. J. (2022) · Electronics · doi:10.3390/electronics11020198 · Cited by 361
Hao, W., Yang, T., & Yang, Q. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3073396 · Cited by 143
Krishnaveni, S., Chen, T. M., Sathiyanarayanan, M., & Amutha, B. (2024) · Cluster Computing · doi:10.1007/s10586-024-04320-x · Cited by 44
Metadata
- Authors
- Saharkhizan, M., Azmoodeh, A., Dehghantanha, A., Choo, K.-K. R., & Parizi, R. M.
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-09
- Volume
- 7
- Issue
- 9
- Pages
- 8852-8859
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3028069824
- Cited by
- 208
- References
- 2
- FWCI
- 14.73
- Type
- journal article
- Language
- en
- Open access
- closed
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- Saharkhizan, M., Azmoodeh, A., Dehghantanha, A., Choo, K.-K. R., & Parizi, R. M. (2020). An Ensemble of Deep Recurrent Neural Networks for Detecting IoT Cyber Attacks Using Network Traffic. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2020.2996425
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