Anomaly behavior analysis for IoT network nodes

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

2 Scopus citations

Abstract

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities. The integration of IoT with Fog and Cloud Computing can bring not only the computational requirements, but they also enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere and at any time. In any IoT application, communications are crucial to deliver the required information, for instance to take actions during crisis events. However, IoT components such as Gateways, usually referred as IoT nodes, will introduce major security challenges as they contribute to increase the attack surface, preventing the IoT to deliver accurate information to final users. In this paper, we present a methodology to develop an Intrusion Detection System based on Anomaly Behavior Analysis to detect when an IoT network node is being compromised. Our preliminary experimental results show that our approach accurately detects known and unknown anomalies due to misuses or cyber-attacks, with high detection rate and low false alarms.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Future Networks and Distributed Systems, ICFNDS 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450371636
DOIs
StatePublished - 1 Jul 2019
Event3rd International Conference on Future Networks and Distributed Systems, ICFNDS 2019 - Paris, France
Duration: 1 Jul 20192 Jul 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Future Networks and Distributed Systems, ICFNDS 2019
Country/TerritoryFrance
CityParis
Period1/07/192/07/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

  • Anomaly Behavior Analysis
  • Internet of Things
  • Intrusion Detection systems

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