Deep learning with dense random neural networks for detecting attacks against IoT-connected home environments

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Abstract

In this paper, we analyze the network attacks that can be launched against IoT gateways, identify the relevant metrics to detect them, and explain how they can be computed from packet captures. We also present the principles and design of a deep learning-based approach using dense random neural networks (RNN) for the online detection of network attacks. Empirical validation results on packet captures in which attacks were inserted show that the Dense RNN correctly detects attacks.

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APA

Brun, O., Yin, Y., Gelenbe, E., Kadioglu, Y. M., Augusto-Gonzalez, J., & Ramos, M. (2018). Deep learning with dense random neural networks for detecting attacks against IoT-connected home environments. In Communications in Computer and Information Science (Vol. 821, pp. 79–89). Springer Verlag. https://doi.org/10.1007/978-3-319-95189-8_8

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