Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT
It addresses security issues for healthcare-IoT systems by detecting DDoS attacks, but it is incremental as it applies CNNs to a specific domain.
This research tackled the problem of detecting anomalies in time series data from environmental sensors in healthcare-IoT by developing a new method using Convolutional Neural Networks (CNNs), achieving 92% accuracy in identifying possible DDoS attacks.
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.