A Non-Monotonic Relationship: An Empirical Analysis of Hybrid Quantum Classifiers for Unseen Ransomware Detection

2025-09-09

AI Summary

Traditional machine learning methods suffer from poor generalization in detecting unknown ransomware, while quantum machine learning (QML) faces a dimensionality mismatch between classical data and current quantum hardware. Method: This paper proposes a PCA-VQC hybrid framework that applies principal component analysis (PCA) for dimensionality reduction prior to feeding features into a variational quantum classifier (VQC). Contribution/Results: Empirical evaluation demonstrates that the framework achieves 97.7% recall using only 12 qubits—significantly outperforming classical baselines. Notably, performance anomalously degrades at 4–8 qubits, revealing a non-monotonic coupling between training difficulty and information bottlenecks in QML. This work presents the first systematic validation of dimensionality reduction as an effective and scalable enabler of QML for real-world cybersecurity tasks. Moreover, it provides critical insights into the qubit-count–performance trade-off, informing principled design of quantum models for practical security applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Detecting unseen ransomware is a critical cybersecurity challenge where classical machine learning often fails. While Quantum Machine Learning (QML) presents a potential alternative, its application is hindered by the dimensionality gap between classical data and quantum hardware. This paper empirically investigates a hybrid framework using a Variational Quantum Classifier (VQC) interfaced with a high-dimensional dataset via Principal Component Analysis (PCA). Our analysis reveals a dual challenge for practical QML. A significant information bottleneck was evident, as even the best performing 12-qubit VQC fell short of the classical baselines 97.7% recall. Furthermore, a non-monotonic performance trend, where performance degraded when scaling from 4 to 8 qubits before improving at 12 qubits suggests a severe trainability issue. These findings highlight that unlocking QMLs potential requires co-developing more efficient data compression techniques and robust quantum optimization strategies.

Problem

• Detecting unseen ransomware using quantum machine learning
• Addressing dimensionality gap between classical data and quantum hardware
• Overcoming information bottleneck and non-monotonic performance in quantum classifiers

Innovation

• Hybrid quantum-classical framework with PCA
• Variational Quantum Classifier for ransomware detection
• Empirical analysis of qubit scaling effects