XTRACTIS FOR homeland security

Temporal Identification of Criminal Profiles & Action Phases from Communications Metadata

Benchmark vs. Random Forests, Boosted Trees & Neural Networks

How to automatically identify risky behavior linked to criminal activities and predict dangerous Homeland Security situations, by analyzing communication metadata from surveillance investigations, without accessing the content of telephone calls?

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Goals & benefits

Identify specific metadata characterizing different criminal activities and enhance expert knowledge by helping intelligence specialists understand the causal relationships between the communication profiles and the roles inside criminal organizations.

Help intelligence services detect attacks as early as possible and understand the underlying strategy of the criminals in order to consider measures to thwart future attacks.

Avoid a large number of false alarms.


We get a Predictive Model that is:


A Decision System composed of 12 unchained gradual rules using only the 24 variables that xtractis identified as significant (out of the 321 potential predictors characterizing the communications metadata).


Good Real Performance for all the 6 External Test scenarios.


Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).

Use Case 2023/01 (v3.2)

Results by
XTRACTIS® GENERATE 12.2.44349 (2023/01)


  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Explained Predictions for 2 cases
  5. Top-Models Benchmark