SpotLight: Detecting Anomalies In Streaming Graphs

Authors:
Dhivya Eswaran Carnegie Mellon University
Christos Faloutsos Carnegie Mellon University
Sudipto Guha Amazon
Nina Mishra Amazon

One Sentence Summary:

The authors propose a randomized sketching-based approach called SpotLight, which guarantees that an anomalous graph is mapped ‘far’ away from ‘normal’ instances in the sketch space with high probability for appropriate choice of parameters.

Abstract:

How do we spot interesting events from e-mail or transportation logs? How can we detect port scan or denial of service attacks from IP-IP communication data? In general, given a sequence of weighted, directed or bipartite graphs, each summarizing a snapshot of activity in a time window, how can we spot anomalous graphs containing the sudden appearance or disappearance of large dense subgraphs (e.g., near bicliques) in near real-time using sublinear memory? To this end, we propose a randomized sketching-based approach called SpotLight, which guarantees that an anomalous graph is mapped ‘far’ away from ‘normal’ instances in the sketch space with high probability for appropriate choice of parameters. Extensive experiments on real-world datasets show that SpotLight (a) improves accuracy by at least 8.4% compared to prior approaches, (b) is fast and can process millions of edges within a few minutes, (c) scales linearly with the number of edges and sketching dimensions and (d) leads to interesting discoveries in practice.

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