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arxiv: 2407.20695 · v1 · pith:P6VFDHFP · submitted 2024-07-30 · cs.LG · cs.CR· cs.CV

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

Mirza Akhi Khatun , Mangolika Bhattacharya , Ciar\'an Eising , Lubna Luxmi Dhirani This is my paper

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classification cs.LG cs.CRcs.CV
keywords seriestimeanomaliescnnsdatadetectenvironmentalhealthcare-iot
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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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