Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions

TL;DR

This paper surveys quantum machine learning techniques applied to cybersecurity, highlighting their potential to improve detection and analysis of threats, while discussing current limitations and future research directions.

Contribution

It provides a comprehensive taxonomy of QML methods for cybersecurity and maps them to various security tasks, offering insights into their advantages and challenges.

Findings

QML techniques can enhance intrusion detection and malware classification.

Quantum models show promise in encrypted traffic analysis.

Limitations include scalability and noise in quantum hardware.

Abstract

The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rendering them unable to keep up. An alternative, Quantum Machine Learning (QML), has recently emerged, making use of computations based on quantum mechanics. It offers better encoding and processing of high-dimensional structures for certain problems. This survey provides a comprehensive overview of QML techniques relevant to the domain of security, such as Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Variational Quantum Circuits (VQCs), and Quantum Generative Adversarial Networks (QGANs), and discusses the contributions of this paper in relation to existing research in the field and how it improves over them. It also maps these methods across supervised, unsupervised, and generative learning paradigms, and to core cybersecurity tasks, including intrusion and anomaly detection, malware and botnet classification, and encrypted-traffic analytics. It also discusses their application in the domain of cloud computing security, where QML can enhance secure and scalable operations. Many limitations of QML in the domain of cybersecurity have also been discussed, along with the directions for addressing them.