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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.20695 |
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| _version_ | 1866911972025434112 |
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| author | Khatun, Mirza Akhi Bhattacharya, Mangolika Eising, Ciarán Dhirani, Lubna Luxmi |
| author_facet | Khatun, Mirza Akhi Bhattacharya, Mangolika Eising, Ciarán Dhirani, Lubna Luxmi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20695 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT Khatun, Mirza Akhi Bhattacharya, Mangolika Eising, Ciarán Dhirani, Lubna Luxmi Machine Learning Cryptography and Security Computer Vision and Pattern Recognition 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. |
| title | Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT |
| topic | Machine Learning Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.20695 |