Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)

Graph Structure Based Anomaly Behavior Detection

Authors
Kai Wang, Danwei Chen
Corresponding Author
Kai Wang
Available Online July 2016.
DOI
https://doi.org/10.2991/iccia-17.2017.90How to use a DOI?
Keywords
Anomaly Detection, Graph Mining, Unsupervised Learning, Social Graph.
Abstract
The analysis of malicious user behavior patterns in social networks has important implications for detecting malicious pages, fraudsters, and financial frauds.Traditional anomaly detection technology general based on classification algorithm using content feature and user behavior feature, but these type of methods are often with low efficiency, data acquisition difficulty and ignoring the network topology information.This paper puts forward a network graph structure based, unsupervised anomaly detection algorithm GBKD-Forest, we extracted three types of structure characteristics, within the Bagging method random sampling features to establish KD-Tree Forest, to isolate the abnormal samples.Evaluation through the experiment, the proposed algorithm in terms of accuracy and AUC is superior to other graph based anomaly detection algorithm and classical classification algorithm, at the same time, the time complexity of this algorithm has a linear relation with the number of nodes, low space complexity is suitable for large-scale network anomaly detection datasets.
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Proceedings
2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
Part of series
Advances in Computer Science Research
Publication Date
July 2016
ISBN
978-94-6252-361-6
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccia-17.2017.90How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Kai Wang
AU  - Danwei Chen
PY  - 2016/07
DA  - 2016/07
TI  - Graph Structure Based Anomaly Behavior Detection
BT  - 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.90
DO  - https://doi.org/10.2991/iccia-17.2017.90
ID  - Wang2016/07
ER  -