Anomaly Detection in IoT Devices Using Quantum Machine Learning

Abstract

The adoption of the Internet of Things in our daily lives has led to an explosion of data. As we stay connected our data is exposed in various networks which can be a huge security concern. It is important to effectively and efficiently detect these anomalies and act accordingly. However, traditional machine learning techniques struggle to process and analyze this voluminous data efficiently. Quantum Machine Learning (QML) offers a promising solution by leveraging its quantum benefits of exponen tial speedup through superposition. This paper explores the application of QML for anomaly detection in IoT networks, highlighting its potential to significantly improve detection accuracy and speed. Ultimately the goal of this research is to build a foun dation for quantum machine learning algorithms showing its potential equipping us for the future of big data.