Graph Structure Based Anomaly Behavior Detection
Kai Wang, Danwei Chen
Available Online July 2016.
- https://doi.org/10.2991/iccia-17.2017.90How to use a DOI?
- Anomaly Detection, Graph Mining, Unsupervised Learning, Social Graph.
- 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.
- 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 SP - 531 EP - 538 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 -