A new social user anomaly behavior detection system based on blockchain and smart contract

Liu, Xingzi, Jiang, Frank and Zhang, Rongbai 2020, A new social user anomaly behavior detection system based on blockchain and smart contract, in ICNSC 2020 : Proceedings of IEEE International Conference on Networking, Sensing and Control, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, N.J., doi: 10.1109/icnsc48988.2020.9238118.

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Title A new social user anomaly behavior detection system based on blockchain and smart contract
Author(s) Liu, Xingzi
Jiang, FrankORCID iD for Jiang, Frank orcid.org/0000-0003-3088-8525
Zhang, Rongbai
Conference name ICNSC - Networking, Sensing and Control. IEEE International Conference (2020 : Nanjing, China (Online)
Conference location Nanjing, China (Online)
Conference dates 30 Oct - 02 Nov. 2020
Title of proceedings ICNSC 2020 : Proceedings of IEEE International Conference on Networking, Sensing and Control
Publication date 2020
Total pages 5
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Place of publication Piscataway, N.J.
Keyword(s) blockchain
isolation forest
smart contract
anomaly behavior
social media
no CORE2020
Summary Inspired from the iForest algorithmic scheme, we propose an iForest-based blockchain social media anomaly behavior detection method via the improved tree algorithm, for the purpose of isolating the anomalous behaviors as an outlier. The model is integrated with the smart contract structure of blockchain. In the overall system, the user data is sent to the intelligent contract for a period of time. After the identification of the abnormal behavior of social media users, the abnormal behavior in blockchain is marked and stored in the abnormal chain. To a certain extent, the scheme protects users' privacy, improves the efficiency and accuracy of iForest anomaly detection, and is more suitable for multi-dimensional heterogenous data-centric social media user behavior detection.
ISBN 9781728168531
Language eng
DOI 10.1109/icnsc48988.2020.9238118
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2020, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145568

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