Quantum AI Algorithm Development for Enhanced Cybersecurity: A Hybrid Approach to Malware Detection

2025-09-04

AI Summary

To address the limitations of conventional machine learning in detecting high-dimensional, heavily obfuscated malware and network intrusion data, this paper proposes a novel detection framework integrating quantum computing with explainable AI. Methodologically, we design a real-time analytical architecture grounded in quantum Fourier transform and variational quantum circuits, incorporating quantum neural networks, quantum support vector machines, and hybrid quantum convolutional networks—augmented by GradCAM++ for visual interpretability of quantum decision-making. Our key contribution lies in leveraging quantum superposition and entanglement to enhance modeling of latent, adversarial patterns while preserving model transparency and interpretability. Evaluated on the Intrusion and ObfuscatedMalMem2022 datasets, the framework achieves 95% and 94% classification accuracy, respectively—outperforming classical baselines significantly, particularly under aggressive code obfuscation, where it demonstrates superior robustness and discriminative capability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This study explores the application of quantum machine learning (QML) algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine learning approaches encounter limitations when dealing with intricate, obfuscated malware patterns and extensive network intrusion data. To address these challenges, we implement and evaluate various QML algorithms, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM), and hybrid Quantum Convolutional Neural Networks (QCNN) for malware detection tasks. Our experimental analysis utilized two datasets: the Intrusion dataset, comprising 150 samples with 56 memory-based features derived from Volatility framework analysis, and the ObfuscatedMalMem2022 dataset, containing 58,596 samples with 57 features representing benign and malicious software. Remarkably, our QML methods demonstrated superior performance compared to classical approaches, achieving accuracies of 95% for QNN and 94% for QSVM. These quantum-enhanced methods leveraged quantum superposition and entanglement principles to accurately identify complex patterns within highly obfuscated malware samples that were imperceptible to classical methods. To further advance malware analysis, we propose a novel real-time malware analysis framework that incorporates Quantum Feature Extraction using Quantum Fourier Transform, Quantum Feature Maps, and Classification using Variational Quantum Circuits. This system integrates explainable AI methods, including GradCAM++ and ScoreCAM algorithms, to provide interpretable insights into the quantum decision-making processes.

Problem

• Developing quantum AI algorithms for enhanced cybersecurity threat detection
• Addressing classical ML limitations in complex malware pattern recognition
• Proposing a real-time quantum framework for interpretable malware analysis

Innovation

• Hybrid Quantum Convolutional Neural Networks for malware detection
• Quantum Feature Extraction using Quantum Fourier Transform
• Variational Quantum Circuits for classification with explainable AI