Random Neural Networks and Deep Learning for Attack Detection at the Edge

Olivier Brun (1) , Yonghua Yin (2)

1 LAAS-SARA - Équipe Services et Architectures pour Réseaux Avancés
2 Imperial College London

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.

Mots clés: Fog Computing; Cybersecurity; IoT; dense random neural network; deep learning; attack detection

Domaines: Réseaux et télécommunications [cs.NI]; Cryptographie et sécurité [cs.CR]

Olivier Brun, Yonghua Yin. Random Neural Networks and Deep Learning for Attack Detection at the Edge. 2019 IEEE International Conference on Fog Computing (ICFC), Jun 2019, Prague, Czech Republic. pp.11-14, ⟨10.1109/ICFC.2019.00009⟩. ⟨hal-02364255⟩