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N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
Open accessAbstract
The proliferation of IoT devices that can be more easily compromised than desktop computers has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for new methods that detect attacks launched from compromised IoT devices and that differentiate between hours- and milliseconds-long IoT-based attacks. In this article, we propose a novel network-based anomaly detection method for the IoT called N-BaIoT that extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic from compromised IoT devices. To evaluate our method, we infected nine commercial IoT devices in our lab with two widely known IoT-based botnets, Mirai and BASHLITE. The evaluation results demonstrated our proposed methods ability to accurately and instantly detect the attacks as they were being launched from the compromised IoT devices that were part of a botnet.
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“[124] 2021 Anomaly detection ML models in non-IID scenarios, including mechanisms to locally rebalance training datasets Cross-device Decentralized Simulation Four-layered FCNN P2PK-SMOTE Synchronous Asynchronous Random N-BaIoT [180]”
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Metadata
- Authors
- Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y.
- Venue
- IEEE Pervasive Computing
- Published
- 2018-07
- Volume
- 17
- Issue
- 3
- Pages
- 12-22
- ISSN
- 1536-1268 (ISSN-L)
- 1558-2590
- OpenAlex
- W2799758613
- Cited by
- 1,483
- References
- 0
- FWCI
- 53.84
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018). N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders. IEEE Pervasive Computing. https://doi.org/10.1109/mprv.2018.03367731
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