Track Learning and Local Tuning in Standalone Deployments

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Checking learning progress and performing local tuning decisions in standalone deployments (see above) is currently in beta.

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Here you find instructions how you can get access to the following features in open-appsec standalone deployments:

  • Check open-appsec machine learning progress locally

  • Get configuration recommendations based on current learning progress level

  • Receive and decide upon tuning suggestions (supervised learning) presented to you by the open-appsec contextual machine-learning engine.

Make sure to read this first to learn more about learning, tuning suggestions and moving from detect to prevent in open-appsec:

Track Learning and Move From Learn/Detect to Preventchevron-right

Prerequisites:

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  • Existing open-appsec deployment (Docker-Compose or Kubernetes) (Linux-embedded are not supported for local tuning in standalone mode.)

  • No Agent Token configured in the deployment (no connection to central WebUI)

  • Make sure that agent already received some traffic already, as otherwise the open-appsec-tuning-tool will not be able to provide any statistics, recommendations, etc.

Installation of the "open-appsec-tuning-tool"

  • Download the open-appsec-local-tuning tool

Using the open-appsec-tuning-tool

  1. Run the open-appsec-tuning-tool to get an overview of the available options:

open-appsec tuning tool main menu
  1. Select among the available options presented:

  • View statistics Select [1] to view current learning statistics, learning progress and receive recommendations for configuration based on those.

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It may take up to 10 min until you see updated metrics here based on new traffic.

statistics view in open-appsec-tuning-tool
  • Manage tuning suggestions for learning Select [2] to view tuning suggestions in case there are some available based on observed traffic and learning state. To perform tuning: - First select a tuning suggestion based on it's ID. - Review the relevant logs presented which allow you to better decide what decision to take for that suggestion. (You also have the option to export those logs into a .csv file.) - Take a decision on that tuning suggestion by setting it to "malicious" or "benign".

see tuning suggestions in open-appsec-tuning-tool
manage tuning suggestions in open-appsec-tuning-tool
  • View tuning decisions Select [3] to view tuning decisions which you already took based on earlier tuning suggestions.

The open-appsec-tuning-tool supports the following optional parameters:

-env {k8s|docker|embedded} set the environment type, by default the tool will try to auto-detect the environment type of the open-appsec deployment

-tuning-host <host[:port]|pod> set the tuning container hostname and optionally also a non-standard port for Docker-based deployments or the pod name for deployments in Kubernetes (see also the -namespace parameter below for setting open-appsec deployment's Kubernetes namespace)

-namespace set the open-appsec deployment namespace (Kubernetes only)

-agent <container|pod> set open-appsec agent container (Docker) or pod (Kubernetes) for open-appsec tuning tool to connect to default is auto-detect

-port set local port on host to use for port-forwarding to the tuning container default is auto-select an available port (Kubernetes)

-help show open-appsec-tuning-tool help

-version show version of the open-appsec-tuning-tool

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