Lightweight Anomaly Detection in WBANs with One-Class SVM

Abstract

Wearable and implanted devices are revolutionizing healthcare through WBANs, enabling real-time, remote monitoring of physiological indicators. However, issues such as sensor malfunctioning, external interference, or cyberattacks on these miniature devices can compromise the effectiveness of these systems, making reliability a critical concern. These sensor failures may result in inaccurate readings, known as anomalies, which, if improperly interpreted, may pose a major risk to a healthcare application using these parameters. This study investigates machine learning techniques for detecting anomalies in WBANs, focusing on One-Class Support Vector Machines (One-Class SVM). We assess the performance of One-Class SVM alongside other advanced anomaly detection methods, including Logistic Regression, Elliptic Envelope, SGD One-Class SVM, and Isolation Forest. For our evaluations, we used a large dataset from the SmartNet AI Lab, encompassing a wide range of WBAN scenarios. The results indicate that One-Class SVM outperforms the other models, achieving an accuracy of 98.52%, and a precision of 99.98%. Unlike the other models, One-Class SVM balances computational efficiency and anomaly detection accuracy, making it ideal for resource-constrained WBANs. By utilizing less power for training and inference, One-Class SVM enhances the energy efficiency of WBANs.