01021nas a2200181   4500000000100000000000100001008004100002260004600043100001700089700001600106700001700122700002200139700002800161700001700189245011400206520049900320020002000819       2018                            d  c07/2018bSpringeraLondon, United Kingdom1 aOlivier Brun1 aYonghua Yin1 aErol Gelenbe1 aY. Murat Kadioglu1 aJavier Augusto-Gonzalez1 aManuel Ramos00aDeep learning with dense random neural networks for detecting attacks against IoT-connected home environments3 a<p>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.</p>
  a978-331995188-1