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

Autor:	Khatun, Mirza Akhi, Bhattacharya, Mangolika, Eising, Ciarán, Dhirani, Lubna Luxmi
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security Computer Science - Computer Vision and Pattern Recognition
Zdroj:	Proceedings of the 12th IEEE International Conference on Healthcare Informatics (IEEE ICHI 2024)
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.20695 Zobrazit plný text záznamu View this record from Arxiv