用量子机器学习提升消费电子设备异常检测能力
Quantum Machine Learning for Anomaly Detection in Consumer Electronics
- 提出通用框架，适配多种量子机器学习算法
- 涵盖监督、无监督、强化学习三类算法应用
- 适合关注量子计算与安全交叉领域的研究者
异常检测在网络安全中至关重要。技术进步带来了网络入侵、金融欺诈、身份盗窃和财产侵犯等新型网络物理威胁。在快速变化的环境中，新类型异常不断涌现，传统机器学习模型难以全面防范。量子机器学习（QML）作为一种新兴的强大计算工具，能更高效地检测异常。本文介绍了QML及其在消费电子产品异常检测中的应用，提出了一种通用框架以支持QML算法在异常检测任务中的部署，并简要讨论了主流的监督、无监督及强化学习型QML算法。同时，通过五个近期案例研究，展示了这些算法在消费电子领域的实际应用。
原文摘要 · Abstract (English)
Anomaly detection is a crucial task in cyber security. Technological advancement brings new cyber-physical threats like network intrusion, financial fraud, identity theft, and property invasion. In the rapidly changing world, with frequently emerging new types of anomalies, classical machine learning models are insufficient to prevent all the threats. Quantum Machine Learning (QML) is emerging as a powerful computational tool that can detect anomalies more efficiently. In this work, we have introduced QML and its applications for anomaly detection in consumer electronics. We have shown a generic framework for applying QML algorithms in anomaly detection tasks. We have also briefly discussed popular supervised, unsupervised, and reinforcement learning-based QML algorithms and included five case studies of recent works to show their applications in anomaly detection in the consumer electronics field.
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