QuantumNetSec: Quantum Machine Learning for Network Security

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.