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arXiv · 2407.20695

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

Mirza Akhi Khatun·Mangolika Bhattacharya·Ciarán Eising·Lubna Luxmi Dhirani
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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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Mirza Akhi Khatun, Mangolika Bhattacharya, Ciarán Eising, Lubna Luxmi Dhirani. 2024-07-30. Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT. https://arxiv.org/abs/2407.20695

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