Metric Forensics: A Multi-Level Approach for Mining Volatile Graphs
published: Oct. 1, 2010, recorded: July 2010, views: 3283
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Advances in data collection and storage capacity have made it increasingly possible to collect highly volatile graph data for analysis. Existing graph analysis techniques are not appropriate for such data, especially in cases where streaming or near-real-time results are required. An example that has drawn significant research interest is the cyber-security domain, where internet communication traces are collected and real-time discovery of events, behaviors, patterns, and anomalies is desired. We propose METRICFORENSICS, a scalable framework for analysis of volatile graphs. METRICFORENSICS combines a multi-level “drill down” approach, a collection of user-selected graph metrics, and a collection of analysis techniques. At each successive level, more sophisticated metrics are computed and the graph is viewed at finer temporal resolutions. In this way, METRICFORENSICS scales to highly volatile graphs by only allocating resources for computationally expensive analysis when an interesting event is discovered at a coarser resolution first. We test METRICFORENSICS on three real-world graphs: an enterprise IP trace, a trace of legitimate and malicious network traffic from a research institution, and the MIT Reality Mining proximity sensor data. Our largest graph has »3M vertices and »32M edges, spanning 4:5 days. The results demonstrate the scalability and capability ofMETRICFORENSICS in analyzing volatile graphs; and highlight four novel phenomena in such graphs: elbows, broken correlations, prolonged spikes, and lightweight stars.
Download slides: kdd2010_henderson_mfml_01.pdf (1.2 MB)
Download slides: kdd2010_henderson_mfml_01.ppt (1.9 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !