XTRACTIS FOR security

Identification of Unmanned Aerial Vehicle Intrusion
based on Wi-Fi Analysis

Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks

How to automatically, efficiently and transparently identify a type of invading Unmanned Aerial Vehicle (UAV) in a civilian environment, based on Wi-Fi traffic data records?

Goals & benefits

Identify the Wi-Fi traffic data characterizing an UAV intrusion. Enhance expert knowledge by helping security specialists understand the causal relationships between specific Radio frequency characteristics, their combination, and the type of UAV.

Help security agents qualify the type of UAV to diagnose the intrusion as early as possible.

Avoid a large number of false alarms thanks to transparent diagnosis, in a context of increasing number of consumer UAVs.

XTRACTIS RESULTS

We get a Predictive Model that is:

Intelligible.

A Decision System composed of 4 unchained gradual rules, each rule using some of the 5 variables that XTRACTIS identified as significant (out of the 54 potential predictors from the two-way radio frequency time series recordings preprocessing).

Robust.

Perfect Real Performance on External Test.

EFFICIENT & OPERATIONAL.

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

full use case

Use Case 2023/01 (v1.0)

Results by
XTRACTIS® GENERATE 12.2.44533 (2023/01)

DOCUMENT CONTENTS

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