Quantum AI for Cybersecurity: A hybrid Quantum-Classical models for attack path analysis

TL;DR

This paper explores hybrid quantum-classical models for cybersecurity, demonstrating that quantum feature representations can improve attack detection recall and class separation, especially with limited data, indicating potential quantum advantages.

Contribution

It introduces a hybrid quantum-classical framework for attack path analysis and shows quantum embeddings can enhance feature representation in cybersecurity tasks.

Findings

Quantum embeddings improve attack recall with limited data.

Classical models outperform with large datasets.

Quantum feature spaces capture complex correlations.

Abstract

Modern cyberattacks are increasingly complex, posing significant challenges to classical machine learning methods, particularly when labeled data is limited and feature interactions are highly non-linear. In this study we investigates the potential of hybrid quantum-classical learning to enhance feature representations for intrusion detection and explore possible quantum advantages in cybersecurity analytics. Using the UNSW-NB15 dataset, network traffic is transformed into structured feature vectors through classical preprocessing and normalization. Classical models, including Logistic Regression and Support Vector Machines with linear and RBF kernels, are evaluated on the full dataset to establish baseline performance under large-sample conditions. Simultaneously, a quantum-enhanced pipeline maps classical features into variational quantum circuits via angle encoding and entangling layers, executed on a CPU-based quantum simulator, with resulting quantum embeddings classified using a classical SVM. Experiments show that while classical models achieve higher overall accuracy with large datasets, quantum-enhanced representations demonstrate superior attack recall and improved class separability when data is scarce, suggesting that quantum feature spaces capture complex correlations inaccessible to shallow classical models. These results highlight the potential of quantum embeddings to improve generalization and representation quality in cybersecurity tasks and provide a reproducible framework for evaluating quantum advantages as quantum hardware and simulators continue to advance.