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QuantumNetSec: Quantum Machine Learning for Network Security

Author

Listed:
  • Diego Abreu
  • David Moura
  • Christian Esteve Rothenberg
  • Antônio Abelém

Abstract

As the digital landscape becomes increasingly complex, traditional cybersecurity measures are struggling to keep pace with the growing sophistication of cyber threats. This escalating challenge calls for new, more robust solutions. In this context, quantum computing emerges as a powerful tool that can change our approach to network security. Our research addresses this by introducing QuantumNetSec, a novel intrusion detection system (IDS) that combines quantum and classical computing techniques. QuantumNetSec employs quantum machine learning (QML) personalized methodologies to analyze network patterns and detect malicious activities. Through detailed experimentation with publicly shared datasets, QuantumNetSec demonstrated superior performance in both binary and multiclass classification tasks. Our findings highlight the significant potential of quantum‐enhanced cybersecurity solutions, showcasing QuantumNetSec's ability to accurately detect a wide range of cyber threats, paving the way for more resilient and effective IDSs in the age of quantum utility.

Suggested Citation

  • Diego Abreu & David Moura & Christian Esteve Rothenberg & Antônio Abelém, 2025. "QuantumNetSec: Quantum Machine Learning for Network Security," International Journal of Network Management, John Wiley & Sons, vol. 35(4), July.
    • Handle: RePEc:wly:intnem:v:35:y:2025:i:4:n:e70018
    DOI: 10.1002/nem.70018
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    References listed on IDEAS

    as
    1. Dhaou Said, 2023. "Quantum Computing and Machine Learning for Cybersecurity: Distributed Denial of Service (DDoS) Attack Detection on Smart Micro-Grid," Energies, MDPI, vol. 16(8), pages 1-11, April.
    2. Jonas Jäger & Roman V. Krems, 2023. "Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
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