QML-IDS: Quantum Machine Learning Intrusion Detection System

The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational power paves the way for creating strategies to mitigate the constantly advancing threats to network integrity. In response to this technolog...

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Vydáno v:Proceedings - IEEE Symposium on Computers and Communications s. 1 - 6
Hlavní autoři: Abreu, Diego, Rothenberg, Christian Esteve, Abelem, Antonio
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 26.06.2024
Témata:
Computer security
Computers
Intrusion detection
Machine learning
Network security
Quantum computing
Quantum Machine Learning
Quantum Network
ISSN:2642-7389
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Shrnutí:The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational power paves the way for creating strategies to mitigate the constantly advancing threats to network integrity. In response to this technological advancement, our research presents QML-IDS, a novel Intrusion Detection System (IDS) that combines quantum and classical computing techniques. QML-IDS employs Quantum Machine Learning (QML) methodologies to analyze network patterns and detect attack activities. Through extensive experimental tests on publicly available datasets, we show that QML-IDS is effective at attack detection and performs well in binary and multiclass classification tasks. Our findings reveal that QML-IDS outperforms classical Machine Learning methods, demonstrating the promise of quantum-enhanced cybersecurity solutions for the age of quantum utility.
ISSN:2642-7389
DOI:10.1109/ISCC61673.2024.10733655