A New Social User Anomaly Behavior Detection System Based on Blockchain and Smart Contract

Liu, X; Jiang, F; Zhang, R

A New Social User Anomaly Behavior Detection System Based on Blockchain and Smart Contract

Liu, X Jiang, F

Zhang, R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC), 2020, 00, pp. 1-5
Issue Date:: 2020-11-04

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Field	Value	Language
dc.contributor.author	Liu, X
dc.contributor.author	Jiang, F https://orcid.org/0000-0003-3765-561X
dc.contributor.author	Zhang, R
dc.date	2020-10-30
dc.date.accessioned	2021-05-24T21:34:43Z
dc.date.available	2021-05-24T21:34:43Z
dc.date.issued	2020-11-04
dc.identifier.citation	2020 IEEE International Conference on Networking, Sensing and Control (ICNSC), 2020, 00, pp. 1-5
dc.identifier.isbn	978-1-7281-6853-1
dc.identifier.uri	http://hdl.handle.net/10453/149172
dc.description.abstract	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.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)
dc.relation.ispartof	IEEE International Conference on Networking, Sensing and Control
dc.relation.isbasedon	10.1109/icnsc48988.2020.9238118
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	A New Social User Anomaly Behavior Detection System Based on Blockchain and Smart Contract
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Nanjing, China
utslib.for	080303 Computer System Security
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
utslib.copyright.status	in_progress	*
pubs.consider-herdc	false
dc.date.updated	2021-05-24T21:34:41Z
pubs.finish-date	2020-11-02
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-10-30
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/149172