Merge branch 'develop' into feat/opt-cybersecurity (22ea4910) · Commits · TFS / controller

expose_ingress_grpc.sh

+1 −1

Original line number	Diff line number	Diff line
		@@ -21,7 +21,7 @@
		export TFS_K8S_NAMESPACE=${TFS_K8S_NAMESPACE:-"tfs"}

		# If not already set, set the list of components you want to build images for, and deploy.
		export TFS_COMPONENTS=${TFS_COMPONENTS:-"context device automation policy service compute monitoring dbscanserving opticalattackmitigator opticalcentralizedattackdetector webui"}
		export TFS_COMPONENTS=${TFS_COMPONENTS:-"context device automation policy service compute monitoring dbscanserving opticalattackmitigator opticalcentralizedattackdetector l3_attackmitigator l3_centralizedattackdetector webui"}

		########################################################################################################################
		# Automated steps start here

manifests/l3_attackmitigatorservice.yaml

+59 −0

Original line number	Diff line number	Diff line
		@@ -32,6 +32,7 @@ spec:
		imagePullPolicy: Always
		ports:
		- containerPort: 10002
		- containerPort: 9192
		env:
		- name: LOG_LEVEL
		value: "DEBUG"
		@@ -53,11 +54,69 @@ apiVersion: v1
		kind: Service
		metadata:
		name: l3-attackmitigatorservice
		labels:
		app: l3-attackmitigatorservice
		spec:
		type: ClusterIP
		selector:
		app: l3-attackmitigatorservice
		ports:
		- name: metrics
		protocol: TCP
		port: 9192
		targetPort: 9192
		- name: grpc
		port: 10002
		targetPort: 10002

		---
		apiVersion: autoscaling/v2
		kind: HorizontalPodAutoscaler
		metadata:
		name: l3-attackmitigatorservice-hpa
		spec:
		scaleTargetRef:
		apiVersion: apps/v1
		kind: Deployment
		name: l3-attackmitigatorservice
		minReplicas: 1
		maxReplicas: 10
		metrics:
		- type: Resource
		resource:
		name: cpu
		target:
		type: Utilization
		averageUtilization: 80
		behavior:
		scaleDown:
		stabilizationWindowSeconds: 120

		---
		apiVersion: monitoring.coreos.com/v1
		kind: ServiceMonitor
		metadata:
		name: tfs-l3-attackmitigatorservice-metric
		labels:
		app: l3-attackmitigatorservice
		#release: prometheus
		#release: prom # name of the release
		# ( VERY IMPORTANT: You need to know the correct release name by viewing
		# the servicemonitor of Prometheus itself: Without the correct name,
		# Prometheus cannot identify the metrics of the Flask app as the target.)
		spec:
		selector:
		matchLabels:
		# Target app service
		#namespace: tfs
		app: l3-attackmitigatorservice # same as above
		#release: prometheus # same as above
		endpoints:
		- port: metrics # named port in target app
		scheme: http
		path: /metrics # path to scrape
		interval: 5s # scrape interval
		namespaceSelector:
		any: false
		matchNames:
		- tfs # namespace where the app is running

manifests/l3_centralizedattackdetectorservice.yaml

+58 −0

Original line number	Diff line number	Diff line
		@@ -32,6 +32,7 @@ spec:
		imagePullPolicy: Always
		ports:
		- containerPort: 10001
		- containerPort: 9192
		env:
		- name: LOG_LEVEL
		value: "DEBUG"
		@@ -53,11 +54,68 @@ apiVersion: v1
		kind: Service
		metadata:
		name: l3-centralizedattackdetectorservice
		labels:
		app: l3-centralizedattackdetectorservice
		spec:
		type: ClusterIP
		selector:
		app: l3-centralizedattackdetectorservice
		ports:
		- name: metrics
		protocol: TCP
		port: 9192
		targetPort: 9192
		- name: grpc
		port: 10001
		targetPort: 10001

		---
		apiVersion: autoscaling/v2
		kind: HorizontalPodAutoscaler
		metadata:
		name: l3-centralizedattackdetectorservice-hpa
		spec:
		scaleTargetRef:
		apiVersion: apps/v1
		kind: Deployment
		name: l3-centralizedattackdetectorservice
		minReplicas: 1
		maxReplicas: 10
		metrics:
		- type: Resource
		resource:
		name: cpu
		target:
		type: Utilization
		averageUtilization: 80
		behavior:
		scaleDown:
		stabilizationWindowSeconds: 120
		---
		apiVersion: monitoring.coreos.com/v1
		kind: ServiceMonitor
		metadata:
		name: tfs-l3-centralizedattackdetectorservice-metric
		labels:
		app: l3-centralizedattackdetectorservice
		#release: prometheus
		#release: prom # name of the release
		# ( VERY IMPORTANT: You need to know the correct release name by viewing
		# the servicemonitor of Prometheus itself: Without the correct name,
		# Prometheus cannot identify the metrics of the Flask app as the target.)
		spec:
		selector:
		matchLabels:
		# Target app service
		#namespace: tfs
		app: l3-centralizedattackdetectorservice # same as above
		#release: prometheus # same as above
		endpoints:
		- port: metrics # named port in target app
		scheme: http
		path: /metrics # path to scrape
		interval: 5s # scrape interval
		namespaceSelector:
		any: false
		matchNames:
		- tfs # namespace where the app is running

proto/l3_attackmitigator.proto

+22 −12

Original line number	Diff line number	Diff line
		@@ -17,10 +17,12 @@ syntax = "proto3";
		import "context.proto";

		service L3Attackmitigator{
		// Sends a greeting
		rpc SendOutput (L3AttackmitigatorOutput) returns (context.Empty) {}
		// Sends another greeting
		// Perform Mitigation
		rpc PerformMitigation (L3AttackmitigatorOutput) returns (context.Empty) {}
		// Get Mitigation
		rpc GetMitigation (context.Empty) returns (context.Empty) {}
		// Get Configured ACL Rules
		rpc GetConfiguredACLRules (context.Empty) returns (ACLRules) {}
		}


		@@ -28,12 +30,20 @@ message L3AttackmitigatorOutput {
		float confidence = 1;
		string timestamp = 2;
		string ip_o = 3;
		string tag_name = 4;
		int32 tag = 5;
		string flow_id = 6;
		string protocol = 7;
		string port_d = 8;
		string ml_id = 9;
		float time_start = 10;
		float time_end = 11;
		string ip_d = 4;
		string tag_name = 5;
		int32 tag = 6;
		string flow_id = 7;
		string protocol = 8;
		string port_o = 9;
		string port_d = 10;
		string ml_id = 11;
		context.ServiceId service_id = 12;
		context.EndPointId endpoint_id = 13;
		float time_start = 14;
		float time_end = 15;
		}

		message ACLRules {
		repeated context.ConfigRule acl_rules = 1;
		}

proto/l3_centralizedattackdetector.proto

+37 −47

Original line number	Diff line number	Diff line
		@@ -14,65 +14,55 @@

		syntax = "proto3";

		import "context.proto";

		service L3Centralizedattackdetector {
		// Sends a greeting
		rpc SendInput (L3CentralizedattackdetectorMetrics) returns (Empty) {}
		// Sends another greeting
		rpc GetOutput (Empty) returns (L3CentralizedattackdetectorModelOutput) {}
		// Analyze single input to the ML model in the CAD component
		rpc AnalyzeConnectionStatistics (L3CentralizedattackdetectorMetrics) returns (Empty) {}

		// Analyze a batch of inputs to the ML model in the CAD component
		rpc AnalyzeBatchConnectionStatistics (L3CentralizedattackdetectorBatchInput) returns (Empty) {}

		// Get the list of features used by the ML model in the CAD component
		rpc GetFeaturesIds (Empty) returns (AutoFeatures) {}
		}

		message Feature {
		float feature = 1;
		}

		message L3CentralizedattackdetectorMetrics {
		/*
		Model input sent to the Inferencer by the client
		There are currently 9 values and
		*/
		// Input sent by the DAD compoenent to the ML model integrated in the CAD component.

		// Machine learning
		float n_packets_server_seconds = 1;
		float n_packets_client_seconds = 2;
		float n_bits_server_seconds = 3;
		float n_bits_client_seconds = 4;
		float n_bits_server_n_packets_server = 5;
		float n_bits_client_n_packets_client = 6;
		float n_packets_server_n_packets_client = 7;
		float n_bits_server_n_bits_client = 8;
		// Machine learning model features
		repeated Feature features = 1;
		ConnectionMetadata connection_metadata = 2;

		// Conection identifier
		string ip_o = 9;
		string port_o = 10;
		string ip_d = 11;
		string port_d = 12;
		string flow_id = 13;
		string protocol = 14;
		float time_start = 15;
		float time_end = 16;
		}

		message Empty {
		string message = 1;
		message ConnectionMetadata {
		string ip_o = 1;
		string port_o = 2;
		string ip_d = 3;
		string port_d = 4;
		string flow_id = 5;
		context.ServiceId service_id = 6;
		context.EndPointId endpoint_id = 7;
		string protocol = 8;
		float time_start = 9;
		float time_end = 10;
		}

		message L3CentralizedattackdetectorModelOutput {
		float confidence = 1;
		string timestamp = 2;
		string ip_o = 3;
		string tag_name = 4;
		int32 tag = 5;
		string flow_id = 6;
		string protocol = 7;
		string port_d = 8;
		string ml_id = 9;
		float time_start = 10;
		float time_end = 11;
		// Collection of values representing ML features
		message AutoFeatures {
		repeated float auto_features = 1;
		}

		// Collections or streams?
		/*
		message InputCollection {
		repeated model_input = 1;
		// Collection (batch) of model inputs that will be sent to the model
		message L3CentralizedattackdetectorBatchInput {
		repeated L3CentralizedattackdetectorMetrics metrics = 1;
		}

		message OutputCollection {
		repeated model_output = 1;
		message Empty {
		string message = 1;
		}
		*/