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GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection...

by Chris R Harshaw, Robert A Bridges, Michael Iannacone, Joel W Reed, John R Goodall
Publication Type
Conference Paper
Publication Date
Conference Name
Cyber & Information Security Research Conference 2016
Conference Location
Oak Ridge, Tennessee, United States of America
Conference Date

This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \textit{GraphPrints}.
Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\textemdash small induced subgraphs that describe local topology.
By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out.
Initial testing of GraphPrints is performed on real network data with an implanted anomaly.
Evaluation shows false positive rates bounded by 2.84\% at the time-interval level, and 0.05\% at the IP-level with 100\% true positive rates at both.