TY - JOUR
T1 - Privacy-preserved Cyberattack Detection in Industrial Edge of Things (IEoT)
T2 - A Blockchain-Orchestrated Federated Learning Approach
AU - Abdel-Basset, Mohamed
AU - Moustafa, Nour
AU - Hawash, Hossam
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - The Industrial Internet of Things (IIoT) plays an essential role in the digital renovation of conventional industries to Industry 4.0. With the connectivity of sensors, actuators, appliances, and other industrial objects, IIoT enables data availability, improved analytics, automatic control. Thanks to the complex distributed nature, a wide range of stealthy and evolving cyberattacks become a major threat to the trustworthiness and security of IIoT systems. This makes the standard security procedures unable to assure the trustworthiness of IIoT that provide protection against cyberattacks. As a remedy, this work presents a Blockchain Orchestrated Edge Intelligence (BoEI) framework that integrates an innovative decentralized federated learning (called Fed-Trust) for cyberattack detection in IIoT. In the Fed-Trust, a Temporal convolutional generative network (TCGAN) is introduced to enable semi-supervised learning from semi-labeled data. BoEI includes reputation-based Blockchain to enable decentralized recording and verification of the transactions for guaranteeing the security and privacy of data and gradients. fog computing is exploited to offload the block mining operation from the edge side thereby improving the overall computation and communication performance of Fed-Trust. Proof of concept simulations using two public datasets validate the robustness and efficiency of the Fed-Trust over the cutting-edge detection approaches.
AB - The Industrial Internet of Things (IIoT) plays an essential role in the digital renovation of conventional industries to Industry 4.0. With the connectivity of sensors, actuators, appliances, and other industrial objects, IIoT enables data availability, improved analytics, automatic control. Thanks to the complex distributed nature, a wide range of stealthy and evolving cyberattacks become a major threat to the trustworthiness and security of IIoT systems. This makes the standard security procedures unable to assure the trustworthiness of IIoT that provide protection against cyberattacks. As a remedy, this work presents a Blockchain Orchestrated Edge Intelligence (BoEI) framework that integrates an innovative decentralized federated learning (called Fed-Trust) for cyberattack detection in IIoT. In the Fed-Trust, a Temporal convolutional generative network (TCGAN) is introduced to enable semi-supervised learning from semi-labeled data. BoEI includes reputation-based Blockchain to enable decentralized recording and verification of the transactions for guaranteeing the security and privacy of data and gradients. fog computing is exploited to offload the block mining operation from the edge side thereby improving the overall computation and communication performance of Fed-Trust. Proof of concept simulations using two public datasets validate the robustness and efficiency of the Fed-Trust over the cutting-edge detection approaches.
KW - Blockchains
KW - Cyberattack Detection
KW - Data models
KW - Federated Learning
KW - Industrial Internet of Things
KW - Industrial Internet of Things (IIoT)
KW - Privacy
KW - Security
KW - Semi-supervised Generative Adversarial Network
KW - Servers
KW - Training
KW - Trustworthiness
KW - industrial Internet of Things (IIoT)
KW - privacy
KW - Cyberattack detection
KW - semi-supervised generative adversarial network (GAN)
KW - trustworthiness
KW - security
KW - federated learning (FL)
UR - http://www.scopus.com/inward/record.url?scp=85128593347&partnerID=8YFLogxK
U2 - 10.1109/TII.2022.3167663
DO - 10.1109/TII.2022.3167663
M3 - Article
AN - SCOPUS:85128593347
SN - 1551-3203
VL - 18
SP - 7920
EP - 7934
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 11
ER -