Device Identification for IoT Security

Phornsawan Roemsri, Rattikorn Hewett

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

Abstract

IoT (Internet of things) devices have become integral parts of our everyday life to function especially in smart homes/offices/cities. However, their huge varieties and scales make it hard to manage the security and the quality of services of IoT networks with generic policies. Furthermore, IoT devices owned by employees can unknowingly endanger the security and integrity of the network from other IoT entities they are connected to. Fortunately, IoT devices of the same type tend to have similar vulnerabilities making attack prevention more manageable. Thus, identifying the device types is crucial for IoT security. This paper presents an approach to building a classifier to identify different types of IoT devices using various machine learning techniques. While most existing empirical studies use network traffic data, we use communication protocol data. The paper describes the data collection/treatment, the experiments using four machine learning techniques and compares our proposed approach with an existing work based on the Jaccard similarity measure. The results show that both approaches have competitive accuracy of up to 99% but the Jaccard approach does not scale well (e.g., about 1000 times more than the training time of our approach).

Original languageEnglish
Title of host publication2021 6th International Conference on Signal and Image Processing, ICSIP 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages866-870
Number of pages5
ISBN (Electronic)9780738133737
DOIs
StatePublished - 2021
Event6th International Conference on Signal and Image Processing, ICSIP 2021 - Nanjing, China
Duration: Oct 22 2021Oct 24 2021

Publication series

Name2021 6th International Conference on Signal and Image Processing, ICSIP 2021

Conference

Conference6th International Conference on Signal and Image Processing, ICSIP 2021
Country/TerritoryChina
CityNanjing
Period10/22/2110/24/21

Keywords

  • Categorical Naïve Bayes
  • Decision tree
  • Extremely randomized trees
  • Internet of things
  • IoT devise
  • Jaccard Index
  • Levenshtein distance
  • Local network
  • Machine learning
  • Naïve Bayes
  • Random forest

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