Graph Stream Mining

http://www.utdallas.edu/~hamlen/parveen-passat11.pdf

Insider Threat Detection using Stream Mining and Graph Mining

Evidence of malicious insider activity is often
buried within large data streams, such as system logs
accumulated over months or years. Ensemble-based stream
mining leverages multiple classification models to achieve
highly accurate anomaly detection in such streams even
when the stream is unbounded, evolving, and unlabeled.
This makes the approach effective for identifying insider threats who attempt to conceal their activities by
varying their behaviors over time. This paper applies
ensemble-based stream mining, unsupervised learning, and
graph-based anomaly detection to the problem of insider
threat detection, demonstrating that the ensemble-based
approach is significantly more effective than traditional
single-model methods.
Index Terms—anomaly d