# QML-IDS: Quantum Machine Learning Intrusion Detection System

arXiv: https://arxiv.org/abs/2410.16308 · HTML: https://mlsanity.com/paper/2410.16308/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2024.

## Built on by (most cited first, 6 of 6)

- [PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier](https://mlsanity.com/paper/2503.01799/) (2025)
- [SQUASH: A SWAP-Based Quantum Attack to Sabotage Hybrid Quantum Neural Networks](https://mlsanity.com/paper/2506.24081/) (2025)
- [Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions](https://mlsanity.com/paper/2512.15286/) (2025)
- [How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2608.18155/) (2026)
- [Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency](https://mlsanity.com/paper/2609.36479/) (2026)
- [Adaptive-Shot Hybrid Quantum Anomaly Detection for Tactile Internet Security: Reliability-Aware Measurement Allocation Under Resource Constraints](https://mlsanity.com/paper/2610.05835/) (2026)

## Abstract (arXiv)

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.