用量子模型分析恶意代码，准确率超90%，探索了量子机器学习新路径。
Towards Quantum Machine Learning for Malicious Code Analysis
- 用角度编码将恶意代码特征转为量子态，结合量子多层感知机和卷积网络
- 二分类准确率达95%以上，多分类最高达95.7%，部分任务仍具挑战性
- 适合对量子计算与安全交叉领域感兴趣的科研人员或前沿探索者
经典机器学习在恶意代码分类中已广泛应用。随着量子计算的发展，量子机器学习（QML）为提升恶意检测提供了范式革新机遇，但该领域研究仍不充分。本文探究两种混合量子-经典模型：量子多层感知机（QMLP）与量子卷积神经网络（QCNN），用于恶意代码分类。两模型均采用角度嵌入将恶意代码特征编码为量子态。QMLP通过全量子比特测量与数据重加载捕捉复杂模式，而QCNN利用量子卷积与池化层减少活跃量子比特，实现更快训练。我们在五个常用恶意代码数据集——API-Graph、EMBER-Domain、EMBER-Class、AZ-Domain 和 AZ-Class 上进行二分类与多分类评估。结果显示，二分类准确率高达95–96%（API-Graph）、91–92%（AZ-Domain）、77%（EMBER-Domain）。多分类中，准确率范围为91.6–95.7%（API-Graph）、41.7–93.6%（AZ-Class）、60.7–88.1%（EMBER-Class）。总体而言，QMLP在复杂多分类任务中表现更优，而QCNN虽精度略低，但训练效率更高。
原文摘要 · Abstract (English)
Classical machine learning (CML) has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning (QML) presents a paradigm-shifting opportunity to improve malware detection, though its application in this domain remains largely unexplored. In this study, we investigate two hybrid quantum-classical models -- a Quantum Multilayer Perceptron (QMLP) and a Quantum Convolutional Neural Network (QCNN), for malware classification. Both models utilize angle embedding to encode malware features into quantum states. QMLP captures complex patterns through full qubit measurement and data re-uploading, while QCNN achieves faster training via quantum convolution and pooling layers that reduce active qubits. We evaluate both models on five widely used malware datasets -- API-Graph, EMBER-Domain, EMBER-Class, AZ-Domain, and AZ-Class, across binary and multiclass classification tasks. Our results show high accuracy for binary classification -- 95-96% on API-Graph, 91-92% on AZ-Domain, and 77% on EMBER-Domain. In multiclass settings, accuracy ranges from 91.6-95.7% on API-Graph, 41.7-93.6% on AZ-Class, and 60.7-88.1% on EMBER-Class. Overall, QMLP outperforms QCNN in complex multiclass tasks, while QCNN offers improved training efficiency at the cost of reduced accuracy.
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