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

Version 2 2024-06-04, 14:49
Version 1 2020-11-21, 14:51
conference contribution
posted on 2024-06-04, 14:49 authored by Xingzi Liu, Frank JiangFrank Jiang, Rongbai Zhang
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.



Nanjing, China (Online)

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Publication classification

E1 Full written paper - refereed

Copyright notice

2020, IEEE

Title of proceedings

ICNSC 2020 : Proceedings of IEEE International Conference on Networking, Sensing and Control


ICNSC - Networking, Sensing and Control. IEEE International Conference (2020 : Nanjing, China (Online)


Institute of Electrical and Electronics Engineers (IEEE)

Place of publication

Piscataway, N.J.