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Author Brun, Olivier ♦ Yin, Yonghua ♦ Gelenbe, Erol
Source Hyper Articles en Ligne (HAL)
Content type Text
File Format PDF
Language English
Subject Keyword dense random neural network ♦ deep learning ♦ Cybersecurity ♦ IoT ♦ info ♦ Computer Science [cs]/Artificial Intelligence [cs.AI] ♦ Computer Science [cs]/Cryptography and Security [cs.CR] ♦ Computer Science [cs]/Networking and Internet Architecture [cs.NI] ♦ Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
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.
Educational Use Research
Learning Resource Type Proceeding
Publisher Date 2018-08-01