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

被引：3

作者：

Brun, Olivier ^{[1
]}

Yin, Yonghua ^{[2
]}

机构：

[1] Univ Toulouse, CNRS, LAAS, Toulouse, France

[2] Imperial Coll, Elect & Elect Engn Dept, Intelligent Syst & Networks Grp, London SW7 2AZ, England

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON FOG COMPUTING (ICFC 2019) | 2019年

基金：

欧盟地平线“2020”;

关键词：

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

D O I：

10.1109/ICFC.2019.00009

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：11 / 14

页数：4

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