{
  "id": "2407.20695",
  "version": "v1",
  "published": "2024-07-30T09:43:42.000Z",
  "updated": "2024-07-30T09:43:42.000Z",
  "title": "Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT",
  "authors": [
    "Mirza Akhi Khatun",
    "Mangolika Bhattacharya",
    "Ciarán Eising",
    "Lubna Luxmi Dhirani"
  ],
  "journal": "Proceedings of the 12th IEEE International Conference on Healthcare Informatics (IEEE ICHI 2024)",
  "categories": [
    "cs.LG",
    "cs.CR",
    "cs.CV"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-07-30T09:43:42.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "time series anomaly detection",
      "time series data",
      "healthcare-iot",
      "convolutional neural networks",
      "cnns detect anomalies"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}