loading page

QuantumNetSec: Quantum Machine Learning for Network Security
  • +1
  • Diego Abreu,
  • David Moura,
  • Christian Rothenberg,
  • Antônio Abelém
Diego Abreu
Universidade Federal do Para

Corresponding Author:[email protected]

Author Profile
David Moura
Universidade Estadual de Campinas
Author Profile
Christian Rothenberg
Universidade Estadual de Campinas
Author Profile
Antônio Abelém
Universidade Federal do Para
Author Profile

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 intrusion detection systems in the age of quantum utility.
31 Aug 2024Submitted to International Journal of Network Management
02 Sep 2024Submission Checks Completed
02 Sep 2024Assigned to Editor
02 Sep 2024Review(s) Completed, Editorial Evaluation Pending
30 Sep 2024Reviewer(s) Assigned