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We introduce a new malware detector - Shape-GD - that aggregates per-machinedetectors into a robust global detector. Shape-GD is based on two insights: 1.Structural: actions such as visiting a website (waterhole attack) by nodescorrelate well with malware spread, and create dynamic neighborhoods of nodesthat were exposed to the same attack vector. However, neighborhood sizes varyunpredictably and require aggregating an unpredictable number of localdetectors' outputs into a global alert. 2. Statistical: feature vectorscorresponding to true and false positives of local detectors have markedlydifferent conditional distributions - i.e. their shapes differ. The shape ofneighborhoods can identify infected neighborhoods without having to estimateneighborhood sizes - on 5 years of Symantec detectors' logs, Shape-GD reducesfalse positives from ~1M down to ~110K and raises alerts 345 days (on average)before commercial anti-virus products; in a waterhole attack simulated usingYahoo web-service logs, Shape-GD detects infected machines when only ~100 of~550K are compromised.

The Shape of Alerts: Detecting Malware Using Distributed Detectors by Robustly Amplifying Transient Correlations