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Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments

Abstract : In this paper, we analyze the network attacks that can be launched against Internet of Things (IoT) gateways, identify the relevant metrics to detect them, and explain how they can be computed from packet captures. We then 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 are inserted show that the Dense RNN correctly detects attacks. However our experiments show that a simple threshold detector also provides results of comparable accuracy on the same data set.
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https://hal.laas.fr/hal-02062117
Contributor : Olivier Brun <>
Submitted on : Friday, March 8, 2019 - 3:38:02 PM
Last modification on : Thursday, March 5, 2020 - 2:43:38 PM
Long-term archiving on: : Monday, June 10, 2019 - 3:01:12 PM

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Olivier Brun, Yonghua Yin, Erol Gelenbe. Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments. First workshop on Secure and Efficient Deployment of IoT (SEDIT 2018), Aug 2018, Gran Canaria, Spain. 6p., ⟨10.1016/j.procs.2018.07.183⟩. ⟨hal-02062117⟩

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