DÏoT: A Federated Self-learning Anomaly Detection System for IoT

T. D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, A. Sadeghi

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

Original languageEnglish
Title of host publication2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)
PublisherIEEE
Pages756-767
Number of pages12
ISBN (Electronic)978-1-7281-2519-0
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Distributed Computing Systems - Dallas, United States
Duration: 7 Jul 201910 Jul 2019
Conference number: 39

Publication series

NameInternational Conference on Distributed Computing Systems
PublisherIEEE
ISSN (Electronic)2575-8411

Conference

ConferenceInternational Conference on Distributed Computing Systems
Abbreviated titleICDCS
CountryUnited States
CityDallas
Period07/07/201910/07/2019

Keywords

  • Internet of Things
  • invasive software
  • learning (artificial intelligence)
  • security of data
  • telecommunication security
  • federated self-learning anomaly detection system
  • vulnerable IoT devices
  • existing intrusion detection techniques
  • DÏoT
  • autonomous self-learning distributed system
  • device-type-specific communication profiles
  • federated learning approach
  • anomaly-detection-based intrusion detection
  • off-the-shelf IoT devices
  • detecting devices
  • Security
  • Logic gates
  • Anomaly detection
  • Malware
  • Data models
  • Monitoring
  • Internet of Things, IoT security, IoT malware, anomaly detection, federated deep learning, self-learning

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