Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems

TL;DR

This paper investigates how quantum machine learning could enhance cybersecurity, specifically in intrusion detection, by analyzing potential advantages and quantifying future impacts with a case study on PCA-based systems.

Contribution

It introduces a methodology to assess the impact of fault-tolerant QML algorithms on cybersecurity, with a case study demonstrating potential quantum advantages in intrusion detection.

Findings

Quantum computing may offer advantages in cybersecurity ML tasks.

A methodology to evaluate future quantum impact is proposed.

Results highlight conditions for quantum advantage in intrusion detection.

Abstract

Quantum computing promises to revolutionize our understanding of the limits of computation, and its implications in cryptography have long been evident. Today, cryptographers are actively devising post-quantum solutions to counter the threats posed by quantum-enabled adversaries. Meanwhile, quantum scientists are innovating quantum protocols to empower defenders. However, the broader impact of quantum computing and quantum machine learning (QML) on other cybersecurity domains still needs to be explored. In this work, we investigate the potential impact of QML on cybersecurity applications of traditional ML. First, we explore the potential advantages of quantum computing in machine learning problems specifically related to cybersecurity. Then, we describe a methodology to quantify the future impact of fault-tolerant QML algorithms on real-world problems. As a case study, we apply our approach to standard methods and datasets in network intrusion detection, one of the most studied applications of machine learning in cybersecurity. Our results provide insight into the conditions for obtaining a quantum advantage and the need for future quantum hardware and software advancements.