XTRACTIS FOR cyber security

Log-based Identification of Cyber Intrusions

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

How to automatically, efficiently and transparently detect an intrusion on a computer network and identify the type of attack, only from the connection logs?

Goals & benefits

Identify the logs characterizing a computer intrusion. Enhance expert knowledge by helping cybersecurity specialists understand the causal relationships between specific logs features, their combination, and the type of intrusion.

Help IT diagnose the type of the cyberattack as early as possible and understand the underlying strategy of the attacker in order to consider measures to thwart future attacks.

Avoid a large number of false alarms.

The attacker will always have the advantage if they use the same AI as the defender. This is why an efficient, non-public AI such as xtractis offers a significant advantage in the defense, security and cybersecurity domains.

Prof. Zyed ZALILA
XTRACTIS RESULTS

We get a Predictive Model that is:

Intelligible.

A Decision System composed of 36 unchained gradual rules, each rule using some of the 27 variables that XTRACTIS identified as significant (out of 41 Potential Predictors characterizing each log).

Robust.

Good 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 2022/10 (v1.5)

Results by
XTRACTIS® GENERATE 12.2.43016 (08/2022)

DOCUMENT CONTENTS

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