Quantum Clustering on Streaming Data: A Novel Method for Analyzing Big Data

Rebecca Hofer, Kevin Mallinger, Kevin Mallinger

2023

Abstract

Quantum Clustering is an efficient unsupervised machine learning method that exploits models of quantum mechanics to discover clusters in data points. We applied an adaption of the algorithm on the CIDDS-001 and IoTID20 network intrusion datasets to distinguish malicious from benign network activity. For this purpose, we integrated Quantum Clustering into the framework of DenStream, adjusting it to the streaming data conditions required for analyzing network data. We found that this significantly improved running time and memory requirements compared to the original version of Quantum Clustering, which is known to have high computational complexity. We also found that the accuracy with which the proposed version detected patterns in network activity was comparable to established methods, confirming the algorithm’s applicability for intrusion detection.

Paper Citation

in Harvard Style

Hofer R. and Mallinger K. (2023). Quantum Clustering on Streaming Data: A Novel Method for Analyzing Big Data. In Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-643-9, SciTePress, pages 17-28. DOI: 10.5220/0011764200003482

in Bibtex Style

@conference{iotbds23,
author={Rebecca Hofer and Kevin Mallinger},
title={Quantum Clustering on Streaming Data: A Novel Method for Analyzing Big Data},
booktitle={Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2023},
pages={17-28},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011764200003482},
isbn={978-989-758-643-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Quantum Clustering on Streaming Data: A Novel Method for Analyzing Big Data
SN - 978-989-758-643-9
AU - Hofer R.
AU - Mallinger K.
PY - 2023
SP - 17
EP - 28
DO - 10.5220/0011764200003482
PB - SciTePress