Classification of device behaviour in internet of things infrastructures

Roman Ferrando, Paul Stacey

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

Abstract

Increasingly Internet of Things (IoT) devices are being woven into the fabric of our physical world. With this rapidly expanding pervasive deployment of IoT devices, and supporting infrastructure, we are fast approaching the point where the problem of IoT based cyber-security attacks is a serious threat to industrial operations, business activity and social interactions that leverage IoT technologies. The number of threats and successful attacks against connected systems using IoT devices and services are increasing. The Internet of Things has several characteristics that present technological challenges to traditional cyber-security techniques. The Internet of Things requires a novel and dynamic security paradigm. This paper describes the challenges of securing the Internet of Things. A discussion detailing the state-of-the-art of IoT security is presented. A novel approach to security detection using streaming data analytics to classify and detect security threats in their early stages is proposed. Implementation methodologies and results of ongoing work to realise this new IoT cyber-security technique for threat detection are presented.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Internet of Things and Machine Learning, IML 2017
EditorsHani Hamdan, Faouzi Hidoussi, Djallel Eddine Boubiche
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450352437
DOIs
Publication statusPublished - 17 Oct 2017
Event1st International Conference on Internet of Things and Machine Learning, IML 2017 - Liverpool, United Kingdom
Duration: 17 Oct 201718 Oct 2017

Publication series

NameACM International Conference Proceeding Series

Conference

Conference1st International Conference on Internet of Things and Machine Learning, IML 2017
Country/TerritoryUnited Kingdom
CityLiverpool
Period17/10/1718/10/17

Keywords

  • Abnormal Behaviour Detection
  • Cyber Security
  • Device Behaviour Classification
  • Internet of Things
  • Streaming Analytics

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