Dashboards for NGINX App Protect

Introduction

NGINX App Protect is a new generation WAF from F5 which is built in accordance with UNIX philosophy such that it does one thing well everything else comes from integrations. NGINX App Protect is extremely good at HTTP traffic security. It inherited a powerful WAF engine from BIG-IP and light footprint and high performance from NGINX. Therefore NGINX App Protect brings fine-grained security to all kinds of insertion points where NGINX use to be either on-premises or cloud-based.

Therefore NGINX App Protect is a powerful and flexible security tool but without any visualization capabilities which are essential for a good security product. As mentioned above everything besides primary functionality comes from integrations. In order to introduce visualization capabilities, I've developed an integration between NGINX App Protect and ELK stack (Elasticsearch, Logstash, Kibana) as one of the most adopted stacks for log collection and visualization. Based on logs from NGINX App Protect ELK generates dashboards to clearly visualize what WAF is doing.

Overview Dashboard

Currently, there are two dashboards available. "Overview" dashboard provides a high-level view of the current situation and also allows to discover historical trends. You are free to select any time period of interest and filter data simply by clicking on values right on the dashboard. Table at the bottom of the dashboard lists all requests within a time frame and allows to see how exactly a request looked like.

False Positives Dashboard

Another useful dashboard called "False Positives" helps to identify false positives and adjust WAF policy based on findings. For example, the chart below shows the number of unique IPs that hit a signature. Under normal conditions when traffic is mostly legitimate "per signature" graphs should fluctuate around zero because legitimate users are not supposed to hit any of signatures. Therefore if there is a spike and amount of unique IPs which hit a signature is close to the total amount of sources then likely there is a false positive and policy needs to be adjusted.

Conclusion

This is an open-source and community-driven project. The more people contribute the better it becomes. Feel free to use it for your projects and contribute code or ideas directly to the repo.

The plan is to make these dashboards suitable for all kinds of F5 WAF flavors including AWAF and EAP. This should be simple because it only requires logstash pipeline adjustment to unify logs format stored in elasticsearch index.

If you have a project for AWAF or EAP going on and would like to use dashboards please feel free to develop and create a pull request with an adjusted logstash pipeline to normalize logs from other WAFs.


Github repo: https://github.com/464d41/f5-waf-elk-dashboards


Feel free to reach me with questions. Good luck!

Published Jun 22, 2020
Version 1.0

1 Comment

  • Hi Mikhail

    I am trying to integrate logstash with app protect. Seems it is not work. Using a filebeat for this one. 

    The yaml file is edited with the correct path, the nginx App protect is on and logging is enabled and log are being sent to /var/log/app_protect/security.logs

    When I search for the security logs don't see anything 

     

     

     

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In this installment of \"How I Did It,\" we continue our journey into AI security. I have documented how I deployed an NVIDIA Morpheus AI infrastructure along with F5's NGINX Plus Ingress Controller to provide secure and scalable external access.

","body":"

Hello! In this installment of \"How I Did It,\" we continue our journey into AI security. Below I have documented how I deployed an NVIDIA Morpheus AI infrastructure along with F5's NGINX Plus Ingress Controller to provide secure and scalable external access.

\n

The NVIDIA Morpheus AI Framework is a cybersecurity framework designed to detect and mitigate threats in real time by leveraging AI and machine learning. It provides tools for deep packet inspection, anomaly detection, and automated response, enabling organizations to protect their data and infrastructure from advanced cyber threats. The framework is optimized for use with NVIDIA GPUs, ensuring high performance and scalability.  Morpheus can run on Kubernetes allowing for scalable, flexible, and efficient management of AI workloads across distributed environments.  In addition to Morpheus, NVIDIA offers several NIM microservices that utilize the same AI engine (Triton Inference server).

\n

NVIDIA NIM (NVIDIA Infrastructure Manager) Microservices is a suite of tools designed to simplify and automate the deployment, management, and scaling of AI and deep learning infrastructure on Kubernetes clusters. It enables seamless integration with NVIDIA GPUs, facilitating efficient resource allocation and optimization for AI workloads.  

\n

NGINX Plus Ingress Controller is architected to provide and manage external connectivity to applications running on Kubernetes. It enhances the standard Kubernetes Ingress capabilities by providing advanced features such as SSL/TLS termination, traffic throttling, and advanced routing based on request attributes. NGINX Plus can also integrate with external authentication services and provide enhanced security features like NGINX App Protect.  The controller is designed to improve the performance, reliability, and security of applications deployed on Kubernetes, making it a popular choice for managing ingress traffic in production environments.

\n

Okay, with the major players identified, let's see How I did it....

\n

Prerequisites

\n\n

Let's Get Started 😀

\n

Download the Helm Charts

\n

Prior to deploying any resources, I used VSCode to first create a folder 'nvidia' and fetched/pulled down Helm charts into individual subfolders for the following: 

\n\n\n

Create and export NGC API Key

\n

From the NVIDIA NGC portal, I navigated to 'Setup' and 'Generate API Key'

\n\n

From the upper right corner I selected '+ Generate API Key' and confirm to create the key.  I copied the key from the screen and store for the next step.

\n\n

From the command line I created an environment variable, API_KEY specifying the copied API key.  This environment variable will be referenced in the NVIDIA helm installs.

\n
export API_KEY=<ngc apikey>
\n

Create Kubernetes Secrets

\n

From the MyF5 Portal, navigate to your subscription details, and download the relevant .JWT file.  With the JWT token in hand I created the docker registry secret.  This will be used to pull NGINX Plus IC from the private registry.

\n
kubectl create secret docker-registry regcred --docker-server=private-registry.nginx.com --docker-username=<jwt token> --docker-password=none 
\n

I also needed to create a Kubernetes TLS secret to store my NGINX hosted endpoints.  For ease, I combined my three cert/key combos into two files.  I used the command below to create the secret.

\n
kubectl create secret tls tls-secret --cert=/users/coward/certificates/combined-cert.pem --key=/users/coward/certificates/combined-key.pem
\n

Deploy NGINX Plus Ingress Controller

\n

Before I deployed the NGINX Helm chart, I first needed to modify a few settings in the 'values.yaml' file.  Since I wanted to deploy NGINX Plus Ingress Controller with (NAP)  I specified a repo image, set the 'nginxplus' to true and enabled NGINX App Protect.

\n\n

I used the below command to deploy the Helm chart.

\n
helm install nginx-ingress ./nginx-ingress
\n

I made note of the ingress controller's EXTERNAL IP and updated my domain's (f5demo.net) DNS records (triton-http.f5demo.net, triton-grpc.f5demo.net, triton-http-metrics.f5demo.net) to reflect the public address.

\n\n

I used the below command to port-forward the ingress controller container to my local machine and accessed the dashboard - localhost:8080/dashboard.html.

\n
kubectl port-forward nginx-ingress-controller-6489bd6546 8080:8080
\n

Deploy Morpheus AI Engine

\n
Update the values.yaml file
\n

I updated the 'values.yaml' file to include FQDN tags for the published services.

\n\n

In addition, I created three separate NGINX virtual server resource objects corresponding to the three Triton server endpoints.  The virtual server resources map the Morpheus AI Engine endpoints, (8000, 8001, 8002) to the already deployed NGINX Ingress Controller.  The contents of each file is included below.

\n
apiVersion: k8s.nginx.org/v1 kind: VirtualServer metadata: name: {{ .Values.tags.http_fqdn }} spec: host: {{ .Values.tags.http_fqdn }} tls: secret: tls-secret gunzip: on upstreams: - name: triton-http service: ai-engine port: 8000 routes: - path: / action: pass: triton-http
apiVersion: k8s.nginx.org/v1 kind: VirtualServer metadata: name: {{ .Values.tags.grpc_fqdn }} spec: host: {{ .Values.tags.grpc_fqdn }} tls: secret: tls-secret upstreams: - name: triton-grpc service: ai-engine port: 8001 type: grpc routes: - path: / action: pass: triton-grpc
apiVersion: k8s.nginx.org/v1 kind: VirtualServer metadata: name: {{ .Values.tags.metrics_fqdn }} spec: host: {{ .Values.tags.metrics_fqdn }} tls: secret: tls-secret gunzip: on upstreams: - name: triton-metrics service: ai-engine port: 8002 routes: - path: / action: pass: triton-metrics
\n

With the above files created, I deployed the Helm chart using the following command.

\n
helm install --set ngc.apiKey=\"$API_KEY\" morpheus-ai ./morpheus-ai-engine
\n

I returned to my NGINX dashboard to verify connectivity with the ingress controller and the upstream Morpheus service. 

\n\n

Deploy Morpheus SDK Client

\n

The SDK Client hosts shared models and data libraries. More importantly, the client provides a platform to deploy Morpheus pipelines.  I deployed the Helm chart using the following command.

\n
helm install --set ngc.apiKey=\"$API_KEY\" morpheus-sdk ./morpheus-sdk-client
\n

Once the sdk-cli-helper pod reaches a running state, use the below commands to connect to the pod and move models/data to the shared volume.  The shared volume will be accessed by the Morpheus MLflow pod to publish and deploy models to the AI engine.

\n
kubectl exec sdk-cli-helper -- cp -RL /workspace/models /common 
kubectl exec sdk-cli-helper -- cp -R /workspace/examples/data /common
\n

 

\n

Deploy Morpheus MLFlow

\n

The Morpheus MLFlow service is used to publish and deploy models to the Morpheus AI engine, (triton inference server).  Once the deployed, I will exec into the pod and publish/deploy models.

\n
helm install --set ngc.apiKey=\"$API_KEY\" morpheus-mflow ./morpheus-mlflow
\n

The Morpheus AI engine Helm chart deploys an instance of Kafka and Zookeeper to facilitate Morpheus pipelines.  To enable pipelines, I created two (2) Kafka topics with the below commands.

\n
kubectl exec deploy/broker -c broker -- kafka-topics.sh --create --bootstrap-server broker:9092  --replication-factor 1  --partitions 3  --topic mytopic
kubectl exec deploy/broker -c broker -- kafka-topics.sh --create --bootstrap-server broker:9092  --replication-factor 1 --partitions 3 --topic mytopic-out
\n

Deploy Models

\n

The final step in the deployment was to connect to the MLFlow pod and publish/deploy a couple models to the Triton Inference server.

\n
kubectl exec -it deploy/mlflow -- bash
\n

Now connected to the pods command line, I took a quick look at the available models in the shared volume.

\n
ls -lrt /common/models
\n

I used the below commands to publish and deploy two models (sid-minibert-onnx, phishing-bert-onnx) from the shared volume.

\n
python publish_model_to_mlflow.py --model_name sid-minibert-onnx --model_directory /common/models/triton-model-repo/sid-minibert-onnx --flavor triton
mlflow deployments create -t triton --flavor triton --name sid-minibert-onnx -m models:/sid-minibert-onnx/1 -C \"version=1\"
python publish_model_to_mlflow.py --model_name phishing-bert-onnx --model_directory /common/models/triton-model-repo/phishing-bert-onnx --flavor triton
mlflow deployments create -t triton --flavor triton --name phishing-bert-onnx -m models:/phishing-bert-onnx/1 -C \"version=1\"
\n

Validate Remote Access to Models

\n

With my Morpheus infrastructure deployed and sitting securely behind my NGINX Ingress Controller, there's nothing left to do but test with a couple of curl commands.  I'll first use curl to reach the inference server's status page.

\n
curl -v -k https://triton-http.f5demo.net/v2/health/ready
\n

Finally, i'll use curl to validate that I can reach one of the hosted models.

\n
curl -k https://triton-http.f5demo.net/v2/models/sid-minibert-onnx/config | jq
\n

Check it Out

\n

Want to get a feel for it before trying yourself?  The video below provides a step-by-step walkthrough of the above deployment.

\n
\n

Additional Links

\n

NVIDIA Morpheus AI Framework

\n

NVIDIA NGC Catalog

\n

NGINX Plus Ingress Controller

\n

NGINX App Protect

\n

How I did it - \"Securing Nvidia Triton Inference Server with NGINX Plus Ingress Controller”

","body@stringLength":"25526","rawBody":"

Hello! In this installment of \"How I Did It,\" we continue our journey into AI security. Below I have documented how I deployed an NVIDIA Morpheus AI infrastructure along with F5's NGINX Plus Ingress Controller to provide secure and scalable external access.

\n

The NVIDIA Morpheus AI Framework is a cybersecurity framework designed to detect and mitigate threats in real time by leveraging AI and machine learning. It provides tools for deep packet inspection, anomaly detection, and automated response, enabling organizations to protect their data and infrastructure from advanced cyber threats. The framework is optimized for use with NVIDIA GPUs, ensuring high performance and scalability.  Morpheus can run on Kubernetes allowing for scalable, flexible, and efficient management of AI workloads across distributed environments.  In addition to Morpheus, NVIDIA offers several NIM microservices that utilize the same AI engine (Triton Inference server).

\n

NVIDIA NIM (NVIDIA Infrastructure Manager) Microservices is a suite of tools designed to simplify and automate the deployment, management, and scaling of AI and deep learning infrastructure on Kubernetes clusters. It enables seamless integration with NVIDIA GPUs, facilitating efficient resource allocation and optimization for AI workloads.  

\n

NGINX Plus Ingress Controller is architected to provide and manage external connectivity to applications running on Kubernetes. It enhances the standard Kubernetes Ingress capabilities by providing advanced features such as SSL/TLS termination, traffic throttling, and advanced routing based on request attributes. NGINX Plus can also integrate with external authentication services and provide enhanced security features like NGINX App Protect.  The controller is designed to improve the performance, reliability, and security of applications deployed on Kubernetes, making it a popular choice for managing ingress traffic in production environments.

\n

Okay, with the major players identified, let's see How I did it....

\n

Prerequisites

\n\n

Let's Get Started 😀

\n

Download the Helm Charts

\n

Prior to deploying any resources, I used VSCode to first create a folder 'nvidia' and fetched/pulled down Helm charts into individual subfolders for the following: 

\n\n\n

Create and export NGC API Key

\n

From the NVIDIA NGC portal, I navigated to 'Setup' and 'Generate API Key'

\n\n

From the upper right corner I selected '+ Generate API Key' and confirm to create the key.  I copied the key from the screen and store for the next step.

\n\n

From the command line I created an environment variable, API_KEY specifying the copied API key.  This environment variable will be referenced in the NVIDIA helm installs.

\nexport API_KEY=<ngc apikey>\n

Create Kubernetes Secrets

\n

From the MyF5 Portal, navigate to your subscription details, and download the relevant .JWT file.  With the JWT token in hand I created the docker registry secret.  This will be used to pull NGINX Plus IC from the private registry.

\nkubectl create secret docker-registry regcred --docker-server=private-registry.nginx.com --docker-username=<jwt token> --docker-password=none \n

I also needed to create a Kubernetes TLS secret to store my NGINX hosted endpoints.  For ease, I combined my three cert/key combos into two files.  I used the command below to create the secret.

\nkubectl create secret tls tls-secret --cert=/users/coward/certificates/combined-cert.pem --key=/users/coward/certificates/combined-key.pem\n

Deploy NGINX Plus Ingress Controller

\n

Before I deployed the NGINX Helm chart, I first needed to modify a few settings in the 'values.yaml' file.  Since I wanted to deploy NGINX Plus Ingress Controller with (NAP)  I specified a repo image, set the 'nginxplus' to true and enabled NGINX App Protect.

\n\n

I used the below command to deploy the Helm chart.

\nhelm install nginx-ingress ./nginx-ingress\n

I made note of the ingress controller's EXTERNAL IP and updated my domain's (f5demo.net) DNS records (triton-http.f5demo.net, triton-grpc.f5demo.net, triton-http-metrics.f5demo.net) to reflect the public address.

\n\n

I used the below command to port-forward the ingress controller container to my local machine and accessed the dashboard - localhost:8080/dashboard.html.

\nkubectl port-forward nginx-ingress-controller-6489bd6546 8080:8080\n

Deploy Morpheus AI Engine

\n
Update the values.yaml file
\n

I updated the 'values.yaml' file to include FQDN tags for the published services.

\n\n

In addition, I created three separate NGINX virtual server resource objects corresponding to the three Triton server endpoints.  The virtual server resources map the Morpheus AI Engine endpoints, (8000, 8001, 8002) to the already deployed NGINX Ingress Controller.  The contents of each file is included below.

\napiVersion: k8s.nginx.org/v1 kind: VirtualServer metadata: name: {{ .Values.tags.http_fqdn }} spec: host: {{ .Values.tags.http_fqdn }} tls: secret: tls-secret gunzip: on upstreams: - name: triton-http service: ai-engine port: 8000 routes: - path: / action: pass: triton-httpapiVersion: k8s.nginx.org/v1 kind: VirtualServer metadata: name: {{ .Values.tags.grpc_fqdn }} spec: host: {{ .Values.tags.grpc_fqdn }} tls: secret: tls-secret upstreams: - name: triton-grpc service: ai-engine port: 8001 type: grpc routes: - path: / action: pass: triton-grpcapiVersion: k8s.nginx.org/v1 kind: VirtualServer metadata: name: {{ .Values.tags.metrics_fqdn }} spec: host: {{ .Values.tags.metrics_fqdn }} tls: secret: tls-secret gunzip: on upstreams: - name: triton-metrics service: ai-engine port: 8002 routes: - path: / action: pass: triton-metrics\n

With the above files created, I deployed the Helm chart using the following command.

\nhelm install --set ngc.apiKey=\"$API_KEY\" morpheus-ai ./morpheus-ai-engine\n

I returned to my NGINX dashboard to verify connectivity with the ingress controller and the upstream Morpheus service. 

\n\n

Deploy Morpheus SDK Client

\n

The SDK Client hosts shared models and data libraries. More importantly, the client provides a platform to deploy Morpheus pipelines.  I deployed the Helm chart using the following command.

\nhelm install --set ngc.apiKey=\"$API_KEY\" morpheus-sdk ./morpheus-sdk-client\n

Once the sdk-cli-helper pod reaches a running state, use the below commands to connect to the pod and move models/data to the shared volume.  The shared volume will be accessed by the Morpheus MLflow pod to publish and deploy models to the AI engine.

\nkubectl exec sdk-cli-helper -- cp -RL /workspace/models /common kubectl exec sdk-cli-helper -- cp -R /workspace/examples/data /common\n

 

\n

Deploy Morpheus MLFlow

\n

The Morpheus MLFlow service is used to publish and deploy models to the Morpheus AI engine, (triton inference server).  Once the deployed, I will exec into the pod and publish/deploy models.

\nhelm install --set ngc.apiKey=\"$API_KEY\" morpheus-mflow ./morpheus-mlflow\n

The Morpheus AI engine Helm chart deploys an instance of Kafka and Zookeeper to facilitate Morpheus pipelines.  To enable pipelines, I created two (2) Kafka topics with the below commands.

\nkubectl exec deploy/broker -c broker -- kafka-topics.sh --create --bootstrap-server broker:9092 --replication-factor 1 --partitions 3 --topic mytopickubectl exec deploy/broker -c broker -- kafka-topics.sh --create --bootstrap-server broker:9092 --replication-factor 1 --partitions 3 --topic mytopic-out\n

Deploy Models

\n

The final step in the deployment was to connect to the MLFlow pod and publish/deploy a couple models to the Triton Inference server.

\nkubectl exec -it deploy/mlflow -- bash\n

Now connected to the pods command line, I took a quick look at the available models in the shared volume.

\nls -lrt /common/models\n

I used the below commands to publish and deploy two models (sid-minibert-onnx, phishing-bert-onnx) from the shared volume.

\npython publish_model_to_mlflow.py --model_name sid-minibert-onnx --model_directory /common/models/triton-model-repo/sid-minibert-onnx --flavor tritonmlflow deployments create -t triton --flavor triton --name sid-minibert-onnx -m models:/sid-minibert-onnx/1 -C \"version=1\"python publish_model_to_mlflow.py --model_name phishing-bert-onnx --model_directory /common/models/triton-model-repo/phishing-bert-onnx --flavor tritonmlflow deployments create -t triton --flavor triton --name phishing-bert-onnx -m models:/phishing-bert-onnx/1 -C \"version=1\"\n

Validate Remote Access to Models

\n

With my Morpheus infrastructure deployed and sitting securely behind my NGINX Ingress Controller, there's nothing left to do but test with a couple of curl commands.  I'll first use curl to reach the inference server's status page.

\ncurl -v -k https://triton-http.f5demo.net/v2/health/ready\n

Finally, i'll use curl to validate that I can reach one of the hosted models.

\ncurl -k https://triton-http.f5demo.net/v2/models/sid-minibert-onnx/config | jq\n

Check it Out

\n

Want to get a feel for it before trying yourself?  The video below provides a step-by-step walkthrough of the above deployment.

\n
\n

Additional Links

\n

NVIDIA Morpheus AI Framework

\n

NVIDIA NGC Catalog

\n

NGINX Plus Ingress Controller

\n

NGINX App Protect

\n

How I did it - \"Securing Nvidia Triton Inference Server with NGINX Plus Ingress Controller”

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In this installment of \"How I Did It,\" we continue our journey into AI security. I have documented how I deployed an NVIDIA Morpheus AI infrastructure along with F5's NGINX Plus Ingress Controller to provide secure and scalable external access.

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How I did it - \"Securing NVIDIA’s Morpheus AI Framework with NGINX Plus Ingress Controller”","moderationData":{"__ref":"ModerationData:moderation_data:338419"},"body":"

Nice work, Greg!

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