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

arXiv: https://arxiv.org/abs/2407.20695 · HTML: https://mlsanity.com/paper/2407.20695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation](https://mlsanity.com/paper/2401.09124/) (2024)

## Abstract (arXiv)

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.