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

Dominic Lightbody (), Duc-Minh Ngo, Andriy Temko, Colin C. Murphy and Emanuel Popovici ()
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Dominic Lightbody: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
Duc-Minh Ngo: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
Andriy Temko: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
Colin C. Murphy: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
Emanuel Popovici: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland

Future Internet, 2024, vol. 16, issue 3, 1-38

Abstract: 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 this area has two key issues: the lack of datasets for training artificial intelligence (AI)-based intrusion detection models and the fact that most existing datasets concentrate only on one type of network traffic. Thus, this study introduces Dragon_Pi, an intrusion detection dataset designed for IoT devices based on side-channel power consumption data. Dragon_Pi comprises a collection of normal and under-attack power consumption traces from separate testbeds featuring a DragonBoard 410c and a Raspberry Pi. Dragon_Slice is trained on this dataset; it is an unsupervised convolutional autoencoder (CAE) trained exclusively on held-out normal slices from Dragon_Pi for anomaly detection. The Dragon_Slice network has two iterations in this study. The original achieves 0.78 AUC without post-processing and 0.876 AUC with post-processing. A second iteration of Dragon_Slice, utilising dropout to further impede the CAE’s ability to reconstruct anomalies, outperforms the original network with a raw AUC of 0.764 and a post-processed AUC of 0.89.

Keywords: IoT time series power dataset; IoT hardware security; IoT intrusion detection; convolutional autoencoder; unsupervised learning; cyber attacks on IoT; explainable AI (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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