TY - GEN
T1 - Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT
AU - Khatun, Mirza Akhi
AU - Bhattacharya, Mangolika
AU - Eising, Ciaran
AU - Dhirani, Lubna Luxmi
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This research develops a new method to detect anomalies in time series data using Convolutional Neural Net-works (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an loT 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.
AB - This research develops a new method to detect anomalies in time series data using Convolutional Neural Net-works (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an loT 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.
KW - Anomaly Detection
KW - Contiki OS
KW - Cooja Simulator
KW - Cyberattack
KW - DDoS Attack
KW - Healthcare-IoT (H-IoT)
KW - Wireless Sensor Network (WSN)
UR - http://www.scopus.com/inward/record.url?scp=85203720656&partnerID=8YFLogxK
U2 - 10.1109/ICHI61247.2024.00075
DO - 10.1109/ICHI61247.2024.00075
M3 - Conference contribution
AN - SCOPUS:85203720656
T3 - Proceedings - 2024 IEEE 12th International Conference on Healthcare Informatics, ICHI 2024
SP - 522
EP - 524
BT - Proceedings - 2024 IEEE 12th International Conference on Healthcare Informatics, ICHI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Healthcare Informatics, ICHI 2024
Y2 - 3 June 2024 through 6 June 2024
ER -