IoT Device Type Identification Using Hybrid Deep Learning Approach for Increased IoT Security

Jiaqi Bao, Bechir Hamdaoui, Weng Keen Wong

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

52 Citations (Scopus)

Abstract

IoT networks can be viewed as collections of Internet-enabled physical devices and objects, embedded with sensor, actuator, computation, storage and communication components, that are capable of connecting and exchanging data to one another. In recent years, organizations have allowed more and more IoT devices to be connected to their networks, thereby increasing their risks of and exposure to security vulnerabilities and threats. Therefore, it is important for such organizations to be able to identify which devices are connected to their network and which ones are legitimate and pose no risk. Leveraging network traffic to identify devices through supervised learning has recently been gaining popularity, where feature information is first extracted by intercepting device traffic and then exploited to provide device classification. The main limitation of prior works is that they can only identify previously seen types of devices, and any newly added device types are treated as abnormal types. In the real world, hundreds of millions of new IoT devices are produced each year, and the lack of a large amount of training data makes a system based solely on supervised learning unrealistic. In this paper, we propose a hybrid supervised and unsupervised learning method that enables secondary classification of unseen device types. Our technique combines deep neural networks with clustering to enable both seen and unseen device classification, and employs autoencoder technique to reduce dimensionality of datasets, thereby providing a good balance between classification accuracy and overhead.

Original languageEnglish
Title of host publication2020 International Wireless Communications and Mobile Computing, IWCMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages565-570
Number of pages6
ISBN (Electronic)9781728131290
DOIs
Publication statusPublished - Jun 2020
Externally publishedYes
Event16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020 - Limassol, Cyprus
Duration: 15 Jun 202019 Jun 2020

Publication series

Name2020 International Wireless Communications and Mobile Computing, IWCMC 2020

Conference

Conference16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
Country/TerritoryCyprus
CityLimassol
Period15/06/2019/06/20

Keywords

  • IoT device type classification
  • IoT security
  • deep learning
  • network traffic monitoring

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