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Digital Fingerprinting Pipeline

Logo for Digital Fingerprinting Pipeline
Features
Description
The DFP Pipeline container image contains a compiled Morpheus pipeline that is designed to do DFP analysis on Azure AD logs being streamed in via Kafka.
Publisher
NVIDIA
Latest Tag
0.2.
Modified
April 11, 2024
Compressed Size
7.37 GB
Multinode Support
No
Multi-Arch Support
No
0.2. (Latest) Security Scan Results

Linux / amd64

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Digital Fingerprinting Pipeline

The DFP Pipeline container image contains a compiled Morpheus pipeline that is designed to do DFP analysis on Azure AD logs being streamed in via Kafka. This container image is part of the Digital Fingerprinting AI Workflow.

This image can be run in one of two modes: Inference or Training.

Inference

In Inference mode, the pipeline will listen to a Kafka topic, pre-process the incoming stream of data, calculate the risk score, and then publish the results to an Elasticsearch table. This is typically started with the following arguments:

--tracking-uri=http://MLFLOW-INSTANCE --num-threads=10 --prometheus-port=8080 inference --kafka-bootstrap=BOOTSTRAP:PORT --kafka-input-topic=TOPIC --elastic-host=ELASTIC_HOSTNAME --elastic-port=PORT --elastic-user USERNAME --elastic-password PASSWORD --elastic-cacrt /etc/ca.crt --elasstic-https

Training

The training mode is meant to be run with a cronjob, but can also be run manually. It will load the data in JSON format from a URL, create a new model for every user is the dataset, and then publish the model to an existing MLFlow instance. The data used for training will then be saved to an S3 compatible object bucket. This is usually started with the following arguments:

--tracking-uri=http://MLFLOW-INSTANCE --num-threads=10 --prometheus-port=8080 load-data-then-train --bucket-name=S3_BUCKET --training-endpoint-url=http://SErVER/SAMPLE_DATA.json --aws-endpoint-url=YOUR_S3_SERVER