01008nas a2200169   4500000000100000008004100001260004600042100001700088700001600105700001700121700002200138700002800160700001700188245011400205520049900319020002000818       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