Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT

Mirza Akhi Khatun, Mangolika Bhattacharya, Ciarán Eising, Lubna Luxmi Dhirani
7/30/2024
cs.LGcs.CRcs.CV

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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.

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Cite this paper

@article{khatun2024time,
  title  = {Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT},
  author = {Mirza Akhi Khatun and Mangolika Bhattacharya and Ciarán Eising and Lubna Luxmi Dhirani},
  year   = {2024},
  eprint = {2407.20695},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.20695v1}
}

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