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Cyber security is one of the most significant technical challenges in currenttimes. Detecting adversarial activities, prevention of theft of intellectualproperties and customer data is a high priority for corporations and governmentagencies around the world. Cyber defenders need to analyze massive-scale,high-resolution network flows to identify, categorize, and mitigate attacksinvolving networks spanning institutional and national boundaries. Many of thecyber attacks can be described as subgraph patterns, with prominent examplesbeing insider infiltrations (path queries), denial of service (parallel paths)and malicious spreads (tree queries). This motivates us to explore subgraphmatching on streaming graphs in a continuous setting. The novelty of our worklies in using the subgraph distributional statistics collected from thestreaming graph to determine the query processing strategy. We introduce a"Lazy Search" algorithm where the search strategy is decided on avertex-to-vertex basis depending on the likelihood of a match in the vertexneighborhood. We also propose a metric named "Relative Selectivity" that isused to select between different query processing strategies. Our experimentsperformed on real online news, network traffic stream and a synthetic socialnetwork benchmark demonstrate 10-100x speedups over selectivity agnosticapproaches.

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs