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Time series anomaly detection with CNN for environmental sensors in Healthcare-IoT
Date
2024
Abstract
This research develops a new method to detect anomalies in time series data using Convolutional Neural Networks (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an IoT network simulator, Cooja, which emulates environmental sensors such as temperature and humidity. CNNs detect anomalies in time series data, resulting in a 92% accuracy in identifying possible attacks.
Supervisor
Description
Publisher
Institute of Electrical and Electronics Engineers
Citation
2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pp. 522-524
Collections
Files
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Khatun_2024_Time.pdf
Adobe PDF, 143.73 KB