Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach

TL;DR

This paper introduces a hybrid PCA-Autoencoder method for real-time anomaly detection in correlated IoT sensor data, improving response speed and reducing false positives in resource-constrained environments.

Contribution

It presents a novel integrated PCA and Autoencoder approach specifically designed for correlated sensor data in IoT systems, enhancing detection efficiency.

Findings

Faster response times compared to standalone Autoencoders.

Fewer false positives in anomaly detection.

Comparable F1 scores to traditional Autoencoder methods.

Abstract

The growing adoption of IoT systems in industries like transportation, banking, healthcare, and smart energy has increased reliance on sensor networks. However, anomalies in sensor readings can undermine system reliability, making real-time anomaly detection essential. While a large body of research addresses anomaly detection in IoT networks, few studies focus on correlated sensor data streams, such as temperature and pressure within a shared space, especially in resource-constrained environments. To address this, we propose a novel hybrid machine learning approach combining Principal Component Analysis (PCA) and Autoencoders. In this method, PCA continuously monitors sensor data and triggers the Autoencoder when significant variations are detected. This hybrid approach, validated with real-world and simulated data, shows faster response times and fewer false positives. The F1 score of the hybrid method is comparable to Autoencoder, with much faster response time which is driven by PCA.