TY - GEN
T1 - Secure and Resilient Artificial Intelligence of Things
T2 - A HoneyNet Approach for Threat Detection and Situational Awareness
AU - Tan, Liang
AU - Yu, Keping
AU - Ming, Fangpeng
AU - Cheng, Xiaofan
AU - Srivastava, Gautam
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Artificial Intelligence of Things (AIoT) is emerging as the future of Industry 4.0 and will be widely applied in consumer, commercial, and industrial fields. In AIoT, intelligent objects (smart devices), smart gateways, and edge/cloud nodes are subject to a large number of security threats and attacks. However, the traditional network security approaches are not fully suitable for AIoT. To address this issue, this article proposes a HoneyNet approach that includes both threat detection and situational awareness to enhance the security and resilience of AIoT. We first design a HoneyNet based on Docker technology that collects data to detect adversaries and monitor their attack behaviors. The collected data are then converted into images and used as samples to train a deep learning model. Finally, the trained model is deployed in AIoT to perform threat detection and provide situational awareness. To validate our scheme, we conduct HoneyNet deployment and model training on the SiteWhere AIoT platform and construct a simulation environment on this platform for threat detection and situational awareness. The experimental results demonstrate the feasibility and effectiveness of our solution.
AB - Artificial Intelligence of Things (AIoT) is emerging as the future of Industry 4.0 and will be widely applied in consumer, commercial, and industrial fields. In AIoT, intelligent objects (smart devices), smart gateways, and edge/cloud nodes are subject to a large number of security threats and attacks. However, the traditional network security approaches are not fully suitable for AIoT. To address this issue, this article proposes a HoneyNet approach that includes both threat detection and situational awareness to enhance the security and resilience of AIoT. We first design a HoneyNet based on Docker technology that collects data to detect adversaries and monitor their attack behaviors. The collected data are then converted into images and used as samples to train a deep learning model. Finally, the trained model is deployed in AIoT to perform threat detection and provide situational awareness. To validate our scheme, we conduct HoneyNet deployment and model training on the SiteWhere AIoT platform and construct a simulation environment on this platform for threat detection and situational awareness. The experimental results demonstrate the feasibility and effectiveness of our solution.
UR - http://www.scopus.com/inward/record.url?scp=85107180426&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107180426&partnerID=8YFLogxK
U2 - 10.1109/MCE.2021.3081874
DO - 10.1109/MCE.2021.3081874
M3 - Article
AN - SCOPUS:85107180426
VL - 11
SP - 69
EP - 78
JO - IEEE Consumer Electronics Magazine
JF - IEEE Consumer Electronics Magazine
SN - 2162-2248
ER -