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

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

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Details

Original languageEnglish
Title of host publication2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)
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

    Research areas

  • 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

ID: 40731201