ACL tracking experiments

        - containerPort: 9192

        env:

        - name: LOG_LEVEL

          value: "INFO"

          value: "DEBUG"

        envFrom:

        - secretRef:

            name: qdb-data

        - containerPort: 9192

        env:

        - name: LOG_LEVEL

          value: "INFO"

          value: "DEBUG"

        readinessProbe:

          exec:

            command: ["/bin/grpc_health_probe", "-addr=:3030"]

#!/bin/bash

# Copyright 2022-2023 ETSI TeraFlowSDN - TFS OSG (https://tfs.etsi.org/)

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#      http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ----- TeraFlowSDN ------------------------------------------------------------

# Set the URL of the internal MicroK8s Docker registry where the images will be uploaded to.

export TFS_REGISTRY_IMAGES="http://localhost:32000/tfs/"

# Set the list of components, separated by spaces, you want to build images for, and deploy.

export TFS_COMPONENTS="context device automation monitoring pathcomp service slice compute webui load_generator"

# Set the tag you want to use for your images.

export TFS_COMPONENTS="context device automation monitoring pathcomp service slice compute webui load_generator l3_attackmitigator l3_centralizedattackdetector"

export TFS_IMAGE_TAG="dev"

# Set the name of the Kubernetes namespace to deploy TFS to.

export TFS_K8S_NAMESPACE="tfs"

# Set additional manifest files to be applied after the deployment

export TFS_EXTRA_MANIFESTS="manifests/nginx_ingress_http.yaml"

# Set the new Grafana admin password

export TFS_GRAFANA_PASSWORD="admin123+"

# Disable skip-build flag to rebuild the Docker images.

export TFS_SKIP_BUILD=""

# ----- CockroachDB ------------------------------------------------------------

# Set the namespace where CockroackDB will be deployed.

export CRDB_NAMESPACE="crdb"

# Set the database username to be used by Context.

export CRDB_USERNAME="tfs"

# Set the database user's password to be used by Context.

export CRDB_PASSWORD="tfs123"

# Set the database name to be used by Context.

export CRDB_DATABASE="tfs"

# Set CockroachDB installation mode to 'single'. This option is convenient for development and testing.

# See ./deploy/all.sh or ./deploy/crdb.sh for additional details

export CRDB_DEPLOY_MODE="single"

# Disable flag for dropping database, if exists.

export CRDB_DROP_DATABASE_IF_EXISTS=""

# Disable flag for re-deploying CockroachDB from scratch.

export CRDB_REDEPLOY=""

# ----- NATS -------------------------------------------------------------------

# Set the namespace where NATS will be deployed.

export NATS_NAMESPACE="nats"

# Disable flag for re-deploying NATS from scratch.

export NATS_REDEPLOY=""

# ----- QuestDB ----------------------------------------------------------------

# If not already set, set the namespace where QuestDB will be deployed.

export QDB_NAMESPACE="qdb"

# If not already set, set the database username to be used by Monitoring.

export QDB_USERNAME="admin"

# If not already set, set the database user's password to be used by Monitoring.

export QDB_PASSWORD="quest"

# If not already set, set the table name to be used by Monitoring.

export QDB_TABLE="tfs_monitoring"

## If not already set, disable flag for dropping table if exists.

#export QDB_DROP_TABLE_IF_EXISTS=""

# If not already set, disable flag for re-deploying QuestDB from scratch.

export QDB_REDEPLOY=""

  // Sends a batch of inputs to the ML model in the CAD component

  rpc SendInputBatch (L3CentralizedattackdetectorModelInput) returns (Empty) {}

  // DAD request of the list of features in CAD

  rpc SendFeatures (Empty) returns (AutoFeatures) {}

}

message Feature {

	repeated Feature features = 1;	

	ConnectionMetadata connection_metadata = 2;

	/*

	string ip_o = 2;

	string port_o = 3;

	string ip_d = 4;

	string port_d = 5;

	string flow_id = 6;

	context.ServiceId service_id = 7;

	context.EndPointId endpoint_id = 8;

	string protocol = 9;

	float time_start = 10;

	float time_end = 11;

	*/

}

message ConnectionMetadata {

	float time_end = 10;

}

// Collection of int values representing ML features

message AutoFeatures {

	repeated float autoFeatures = 1;

}

// Collection (batcb) of model inputs that will be sent to the model

message L3CentralizedattackdetectorModelInput {

	repeated L3CentralizedattackdetectorMetrics metrics = 1;

            # TODO: use of datastores (might be virtual ones) to enable rollbacks

            resources_to_set, resources_to_delete = compute_rules_to_add_delete(device, request)

            for resource in resources_to_set:

                LOGGER.debug('Resource to set: %s', resource)

            errors = []

            errors.extend(configure_rules(device, driver, resources_to_set))

            errors.extend(deconfigure_rules(device, driver, resources_to_delete))