TY - GEN
T1 - Zero-Trust Empowered Decentralized Security Defense against Poisoning Attacks in SL-IoT
T2 - 2023 IEEE Global Communications Conference, GLOBECOM 2023
AU - Song, Rongxuan
AU - Wu, Jun
AU - Pan, Qianqian
AU - Imran, Muhammad
AU - Naser, Niddal
AU - Jones, Rebet
AU - Verikoukis, Christos
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Swarm learning (SL) exploits the blockchain to realize a federated and decentralized learning, which is very suitable for internet of things (IoT). Different from FL using central server to update global parameter, SL using edge node (header) to do that. However, poisoning attack is also an unresolved problem to SL. Because if header is malicious, it can pollute global parameter more easily than edge nodes. Moreover, there are following important limitations in existing defense schemes for FL, which cannot be used in SL directly. First, existing defense schemes focus on building a whitelist, which obstructs the decentralization because it can just provide decentralization in honest nodes instead of all of nodes. Second, existing schemes just consider poisoning attacks from edge nodes, they cannot defend attacks from header. Third, most existing schemes will let server execute the defense algorithm, but in SL, malicious header can return wrong defense results to deceive managers. To address above challenges, in this paper, we propose a protection system that leverages the concept of zero-trust architecture for SL, which achieves continuous risk calculation, analysis of learning behavior and abnormal parameter detection based on Manhattan distance and accuracy difference of parameters. We also evaluate the performance in the presence of random and customized malicious edge nodes. Experimental results demonstrate that our scheme can achieve higher accuracy than the other existing schemes.
AB - Swarm learning (SL) exploits the blockchain to realize a federated and decentralized learning, which is very suitable for internet of things (IoT). Different from FL using central server to update global parameter, SL using edge node (header) to do that. However, poisoning attack is also an unresolved problem to SL. Because if header is malicious, it can pollute global parameter more easily than edge nodes. Moreover, there are following important limitations in existing defense schemes for FL, which cannot be used in SL directly. First, existing defense schemes focus on building a whitelist, which obstructs the decentralization because it can just provide decentralization in honest nodes instead of all of nodes. Second, existing schemes just consider poisoning attacks from edge nodes, they cannot defend attacks from header. Third, most existing schemes will let server execute the defense algorithm, but in SL, malicious header can return wrong defense results to deceive managers. To address above challenges, in this paper, we propose a protection system that leverages the concept of zero-trust architecture for SL, which achieves continuous risk calculation, analysis of learning behavior and abnormal parameter detection based on Manhattan distance and accuracy difference of parameters. We also evaluate the performance in the presence of random and customized malicious edge nodes. Experimental results demonstrate that our scheme can achieve higher accuracy than the other existing schemes.
KW - Malicious user detection
KW - Poisoning attack
KW - Swarm Learning
UR - http://www.scopus.com/inward/record.url?scp=85187350353&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85187350353&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM54140.2023.10437789
DO - 10.1109/GLOBECOM54140.2023.10437789
M3 - Conference contribution
AN - SCOPUS:85187350353
T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM
SP - 2766
EP - 2771
BT - GLOBECOM 2023 - 2023 IEEE Global Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 December 2023 through 8 December 2023
ER -