Anomaly Detection in IoT:Methods, Techniques and Tools

  1. Laura Victoria Vigoya Morales
  2. Manuel López-Vizcaíno 1
  3. Diego Fernández Iglesias 1
  4. Víctor Manuel Carneiro Díaz 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Book:
XoveTIC 2019: The 2nd XoveTIC Conference (XoveTIC 2019), A Coruña, Spain, 5–6 September
  1. Alberto Alvarellos González (ed. lit.)
  2. José Joaquim de Moura Ramos (ed. lit.)
  3. Beatriz Botana Barreiro (ed. lit.)
  4. Javier Pereira Loureiro (ed. lit.)
  5. Manuel F. González Penedo (ed. lit.)

Publisher: MDPI

ISBN: 978-3-03921-444-0,  978-3-03921-443-3

Year of publication: 2019

Congress: XoveTIC (2. 2019. A Coruña)

Type: Conference paper

DIALNET GOOGLE SCHOLAR lock_openOpen access editor

Abstract

Nowadays, the Internet of things (IoT) network, as system of interrelated computing devices with the ability to transfer data over a network, is present in many scenarios of everyday life. Understanding how traffic behaves can be done more easily if the real environment is replicated to a virtualized environment. In this paper, we propose a methodology to develop a systematic approach to dataset analysis for detecting traffic anomalies in an IoT network. The reader will become familiar with the specific techniques and tools that are used. The methodology will have five stages: definition of the scenario, injection of anomalous packages, dataset analysis, implementation of classification algorithms for anomaly detection and conclusions.

Data source: Dialnet