Summary of Time Series Anomaly Detection with Cnn For Environmental Sensors in Healthcare-iot, by Mirza Akhi Khatun et al.
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT
by Mirza Akhi Khatun, Mangolika Bhattacharya, Ciarán Eising, Lubna Luxmi Dhirani
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method uses Convolutional Neural Networks (CNNs) to detect anomalies in time series data from healthcare-IoT networks. The approach creates a Distributed Denial of Service (DDoS) attack using the Cooja IoT network simulator, which emulates environmental sensors like temperature and humidity. By leveraging CNNs, the method achieves an impressive 92% accuracy in identifying potential attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new method helps detect anomalies in time series data from healthcare-IoT networks. It uses a special kind of artificial intelligence called Convolutional Neural Networks (CNNs). The method simulates a type of cyber attack called a Distributed Denial of Service (DDoS) using the Cooja simulator, which mimics environmental sensors like temperature and humidity. This helps identify potential attacks with 92% accuracy. |
Keywords
* Artificial intelligence * Temperature * Time series
