Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT
<|reference_start|>Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT: 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.<|reference_end|>
@article{khatun2024time,
    title={Time Series Anomaly Detection with CNN for Environmental Sensors in
  Healthcare-IoT},
    author={Mirza Akhi Khatun, Mangolika Bhattacharya, Ciar'an Eising, Lubna
  Luxmi Dhirani},
    journal={Proceedings of the 12th IEEE International Conference on
  Healthcare Informatics (IEEE ICHI 2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20695},
    primaryClass={cs.LG cs.CR cs.CV}
}