Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection

Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection

The growth of the Internet of Things (IoT) has led to a significant rise in cyber attacks and an expanded attack surface for the average consumer. In order to protect consumers and infrastructure, research into detecting malicious IoT activity must be of the highest priority. Security research in th...

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Bibliographic Details
Main Authors: Dominic Lightbody, Duc-Minh Ngo, Andriy Temko, Colin C. Murphy, Emanuel Popovici
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Future Internet
Subjects:
IoT time series power dataset
IoT hardware security
IoT intrusion detection
convolutional autoencoder
unsupervised learning
cyber attacks on IoT
Online Access:https://www.mdpi.com/1999-5903/16/3/88

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https://www.mdpi.com/1999-5903/16/3/88

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