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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Published in:Proceedings - IEEE Symposium on Computers and Communications pp. 1 - 6
Main Authors: Abreu, Diego, Rothenberg, Christian Esteve, Abelem, Antonio
Format: Conference Proceeding
Language:English
Published: IEEE 26.06.2024
Subjects:
Computer security
Computers
Intrusion detection
Machine learning
Network security
Quantum computing
Quantum Machine Learning
Quantum Network
ISSN:2642-7389
Online Access:Get full text
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Abstract 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.
AbstractList 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.
Author Rothenberg, Christian Esteve
Abelem, Antonio
Abreu, Diego
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  givenname: Christian Esteve
  surname: Rothenberg
  fullname: Rothenberg, Christian Esteve
  organization: University of Campinas (UNICAMP)
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  givenname: Antonio
  surname: Abelem
  fullname: Abelem, Antonio
  organization: Federal University of Pará (UFPA)
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Snippet The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational...
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SubjectTerms Computer security
Computers
Intrusion detection
Machine learning
Network security
Quantum computing
Quantum Machine Learning
Quantum Network
Title QML-IDS: Quantum Machine Learning Intrusion Detection System
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