Local Behavior Analysis for Service Object based on Intelligent Space

DOI

10.2991/meic-14.2014.228How to use a DOI?

Keywords

Collusive attack detection; Reputation Aggregation; Relationship; Social Network; Collusion factor

Abstract

According to the position behavior characteristics of service object, this paper firstly determines the environmental mark point by combining the position point clustering with map topological node. By using the state of family environmental mark point to transfer fitting continuous position trajectory, the overall position and behavior pattern of of service object can be analyzed; the possible target position and residence time, and whether it’s in an abnormal state can be determined. At the same time, because of the high computational complexity and poor real-time performance existing in the traditional modeling and identification process, it further introduces the environmental mark point state residence time distribution, combined with the retention time of service object in different markers region, to optimization the model. Finally, this paper detects the service object position, and the experiments show that the model can depict the position movement well, and can help analyze and judge according to the anomaly detection results.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haitao Li
AU  - Hanqing Zhang
PY  - 2014/11
DA  - 2014/11
TI  - Local Behavior Analysis for Service Object based on Intelligent Space
BT  - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 1020
EP  - 1024
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-14.2014.228
DO  - 10.2991/meic-14.2014.228
ID  - Li2014/11
ER  -