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

IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifi...

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Vydané v:	Proceedings of the International Conference on Distributed Computing Systems s. 756 - 767
Hlavní autori:	Nguyen, Thien Duc, Marchal, Samuel, Miettinen, Markus, Fereidooni, Hossein, Asokan, N., Sadeghi, Ahmad-Reza
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.07.2019
Predmet:	Anomaly detection Data models federated deep learning Internet of Things IoT malware IoT security Logic gates Malware Monitoring Security self-learning
ISSN:	2575-8411
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