Quantum Machine Learning for Anomaly Detection in Consumer Electronics

TL;DR

This paper explores the use of Quantum Machine Learning (QML) for anomaly detection in consumer electronics, proposing a generic framework and reviewing recent case studies to demonstrate its effectiveness against cyber-physical threats.

Contribution

It introduces a generic QML framework for anomaly detection and reviews recent applications, highlighting its potential advantages over classical methods.

Findings

QML can detect anomalies more efficiently than classical models.

A generic framework for applying QML in anomaly detection is proposed.

Five recent case studies demonstrate practical applications in consumer electronics.

Abstract

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.