Iot device identification using deep learning

Jaidip Kotak, Yuval Elovici

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

The growing use of IoT devices in organizations has increased the number of attack vectors available to attackers due to the less secure nature of the devices. The widely adopted bring your own device (BYOD) policy which allows an employee to bring any IoT device into the workplace and attach it to an organization’s network also increases the risk of attacks. In order to address this threat, organizations often implement security policies in which only the connection of white-listed IoT devices is permitted. To monitor adherence to such policies and protect their networks, organizations must be able to identify the IoT devices connected to their networks and, more specifically, to identify connected IoT devices that are not on the white-list (unknown devices). In this study, we applied deep learning on network traffic to automatically identify IoT devices connected to the network. In contrast to previous work, our approach does not require that complex feature engineering be applied on the network traffic, since we represent the “communication behavior” of IoT devices using small images built from the IoT devices’ network traffic payloads. In our experiments, we trained a multiclass classifier on a publicly available dataset, successfully identifying 10 different IoT devices and the traffic of smartphones and computers, with over 99% accuracy. We also trained multiclass classifiers to detect unauthorized IoT devices connected to the network, achieving over 99% overall average detection accuracy.

Original languageEnglish
Title of host publication13th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2020
EditorsÁlvaro Herrero, Carlos Cambra, Daniel Urda, Javier Sedano, Héctor Quintián, Emilio Corchado
PublisherSpringer Science and Business Media Deutschland GmbH
Pages76-86
Number of pages11
ISBN (Print)9783030578046
DOIs
StatePublished - 1 Jan 2021
Event13th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2020 - Burgos, Spain
Duration: 16 Sep 202018 Sep 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1267 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference13th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2020
Country/TerritorySpain
CityBurgos
Period16/09/2018/09/20

Keywords

  • Cyber security
  • Deep learning
  • Internet of Things (IoT)
  • IoT device identification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science (all)

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