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Conference papers
Random Neural Networks and Deep Learning for Attack Detection at the Edge
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.
Cited literature [20 references]
https://hal.laas.fr/hal-02364255
Contributor : Olivier Brun Connect in order to contact the contributor
Submitted on : Thursday, November 14, 2019 - 6:40:54 PM
Last modification on : Wednesday, November 3, 2021 - 7:15:51 AM
Long-term archiving on: : Saturday, February 15, 2020 - 4:52:21 PM
Contributor : Olivier Brun Connect in order to contact the contributor
Submitted on : Thursday, November 14, 2019 - 6:40:54 PM
Last modification on : Wednesday, November 3, 2021 - 7:15:51 AM
Long-term archiving on: : Saturday, February 15, 2020 - 4:52:21 PM
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icfc2019-cybersecurity.pdf
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