  Behavioral malware detectors promise to expose previously unknown malware and
are an important security primitive. However, even the best behavioral
detectors suffer from high false positives and negatives. In this paper, we
address the challenge of aggregating weak per-device behavioral detectors in
noisy communities (i.e., ones that produce alerts at unpredictable rates) into
an accurate and robust global anomaly detector (GD).
  Our system - Shape GD - combines two insights: Structural: actions such as
visiting a website (waterhole attack) or membership in a shared email thread
(phishing attack) by nodes correlate well with malware spread, and create
dynamic neighborhoods of nodes that were exposed to the same attack vector; and
Statistical: feature vectors corresponding to true and false positives of local
detectors have markedly different conditional distributions. We use
neighborhoods to amplify the transient low-dimensional structure that is latent
in high-dimensional feature vectors - but neighborhoods vary unpredictably, and
we use shape to extract robust neighborhood-level features that identify
infected neighborhoods.
  Unlike prior works that aggregate local detectors' alert bitstreams or
cluster the feature vectors, Shape GD analyzes the feature vectors that led to
local alerts (alert-FVs) to separate true and false positives. Shape GD first
filters these alert-FVs into neighborhoods and efficiently maps a
neighborhood's alert-FVs' statistical shapes into a scalar score. Shape GD then
acts as a neighborhood level anomaly detector - training on benign program
traces to learn the ShapeScore of false positive neighborhoods, and classifying
neighborhoods with anomalous ShapeScores as malicious.
  Shape GD detects malware early (~100 infected nodes in a ~100K node system
for waterhole and ~10 of 1000 for phishing) and robustly (with ~100% global TP
and ~1% global FP rates).
