Social Relationships and Temp-Spatial Behaviors Based Community Discovery to Improve Cyber Security Practices

Journal article


Cao, J., Weijia, L., Cao, B., Wang, P., Shancang, L., Liu, B. and Iqbal, M. (2019). Social Relationships and Temp-Spatial Behaviors Based Community Discovery to Improve Cyber Security Practices. IEEE Access.
AuthorsCao, J., Weijia, L., Cao, B., Wang, P., Shancang, L., Liu, B. and Iqbal, M.
Abstract

Cyber security significantly relies on the dynamic communities in social networks. The location-based social network (LBSN) is a new type of social system that has sprung up recently that. It turns traditional social networks into heterogeneous networks by incorporating location information, which is used as the medium between the real world and the online social networks, thus bringing new challenges to the community discovery problems. This paper proposes a LBSN homogeneous network model (LSHNM) based on the user social relations and temp-spatial behaviors to calculate the user similarity relations in multi-dimensional features and construct LBSN isomorphism network topology, which can be used to improve cyber security practices. After that non-negative matrix decomposition (NMF) is used to find communities from above isomorphism network topology. The experimental results show that the LSHNM can find more satisfactory community structures.

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Year2019
JournalIEEE Access
PublisherIEEE
ISSN2169-3536
Digital Object Identifier (DOI)doi:10.1109/ACCESS.2019.2931937
Publication dates
Online30 Jul 2019
Publication process dates
Accepted25 Jul 2019
Deposited20 Nov 2019
Publisher's version
License
CC BY 4.0
File Access Level
Open
Supplemental file
File Access Level
Open
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https://openresearch.lsbu.ac.uk/item/88848

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