Quantum Machine Learning for Anomaly Detection in Consumer Electronics
This work addresses cyber-physical threats like network intrusion and fraud in consumer electronics, but it appears incremental as it reviews and applies existing QML methods without new experimental results.
The paper tackles the problem of anomaly detection in consumer electronics by introducing Quantum Machine Learning (QML) as a more efficient computational tool, presenting a generic framework and case studies to demonstrate its applications.
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.