Improving security in IoT-based human activity recognition: a correlation-based anomaly detection approach

• Main Authors: Fan, Jiani, Liu, Ziyao, Du, Hongyang, Kang, Jiawen, Niyato, Dusit, Lam, Kwok-Yan
• 其他作者: College of Computing and Data Science
• 格式: Article
• 語言: English
• 出版: 2025
• 主題: Computer and Information Science; Human activity recognition; Anomaly detection; Monitoring; Internet of Things; Data models; Context modelling; Correlation; Security; Accuracy; Predictive models
• 在線閱讀: https://hdl.handle.net/10356/182538
• 機構: Nanyang Technological University

Anomaly detection in Human Activity Recognition (HAR) is a critical subfield that leverages data from the Internet of Things (IoT) to monitor human activities and detect errors or abnormal events. Conventional rule-based approaches often fail to capture the intricate relationships between sensor val...