# Copyright 2021-2023 H2020 TeraFlow (https://www.teraflow-h2020.eu/) # # 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. from __future__ import print_function from datetime import datetime import os import grpc import numpy as np import onnxruntime as rt import logging from common.proto.l3_centralizedattackdetector_pb2 import Empty from common.proto.l3_centralizedattackdetector_pb2_grpc import L3CentralizedattackdetectorServicer from common.proto.l3_attackmitigator_pb2 import L3AttackmitigatorOutput from common.proto.l3_attackmitigator_pb2_grpc import L3AttackmitigatorStub # KPIs and Monitoring from common.proto.monitoring_pb2 import KpiDescriptor from common.proto.kpi_sample_types_pb2 import KpiSampleType # from monitoring.client.MonitoringClient import MonitoringClient from monitoring.client.MonitoringClient import MonitoringClient from common.proto.monitoring_pb2 import Kpi from common.tools.timestamp.Converters import timestamp_utcnow_to_float LOGGER = logging.getLogger(__name__) here = os.path.dirname(os.path.abspath(__file__)) MODEL_FILE = os.path.join(here, "ml_model/crypto_5g_rf_spider_features.onnx") classification_threshold = os.getenv("CAD_CLASSIFICATION_THRESHOLD", 0.5) class l3_centralizedattackdetectorServiceServicerImpl(L3CentralizedattackdetectorServicer): def __init__(self): LOGGER.debug("Creating Servicer...") self.inference_values = [] self.model = rt.InferenceSession(MODEL_FILE) self.input_name = self.model.get_inputs()[0].name self.label_name = self.model.get_outputs()[0].name self.prob_name = self.model.get_outputs()[1].name self.monitoring_client = MonitoringClient() self.predicted_class_kpi_id = None self.class_probability_kpi_id = None def create_predicted_class_kpi(self, client: MonitoringClient, service_id): # create kpi kpi_description: KpiDescriptor = KpiDescriptor() kpi_description.kpi_description = "L3 security status of service {}".format(service_id) # kpi_description.service_id.service_uuid.uuid = service_id kpi_description.service_id.service_uuid.uuid = str(service_id) kpi_description.kpi_sample_type = KpiSampleType.KPISAMPLETYPE_UNKNOWN new_kpi = client.SetKpi(kpi_description) LOGGER.info("Created Predicted Class KPI {}...".format(new_kpi.kpi_id)) return new_kpi def create_class_prob_kpi(self, client: MonitoringClient, service_id): # create kpi kpi_description: KpiDescriptor = KpiDescriptor() kpi_description.kpi_description = "L3 security status of service {}".format(service_id) kpi_description.service_id.service_uuid.uuid = service_id kpi_description.kpi_sample_type = KpiSampleType.KPISAMPLETYPE_UNKNOWN new_kpi = client.SetKpi(kpi_description) LOGGER.info("Created Class Probability KPI {}...".format(new_kpi.kpi_id)) return new_kpi def make_inference(self, request): # ML MODEL x_data = np.array( [ [ request.c_pkts_all, request.c_ack_cnt, request.c_bytes_uniq, request.c_pkts_data, request.c_bytes_all, request.s_pkts_all, request.s_ack_cnt, request.s_bytes_uniq, request.s_pkts_data, request.s_bytes_all, ] ] ) predictions = self.model.run([self.prob_name], {self.input_name: x_data.astype(np.float32)})[0] # Output format output_message = { "confidence": None, "timestamp": datetime.now().strftime("%d/%m/%Y %H:%M:%S"), "ip_o": request.ip_o, "ip_d": request.ip_d, "tag_name": None, "tag": None, "flow_id": request.flow_id, "protocol": request.protocol, "port_o": request.port_o, "port_d": request.port_d, "ml_id": "RandomForest", "service_id": request.service_id, "time_start": request.time_start, "time_end": request.time_end, } if predictions[0][1] >= classification_threshold: output_message["confidence"] = predictions[0][1] output_message["tag_name"] = "Crypto" output_message["tag"] = 1 else: output_message["confidence"] = predictions[0][0] output_message["tag_name"] = "Normal" output_message["tag"] = 0 return L3AttackmitigatorOutput(**output_message) def SendInput(self, request, context): # PERFORM INFERENCE WITH SENT INPUTS logging.debug("") print("Inferencing ...", flush=True) # STORE VALUES self.inference_values.append(request) # MAKE INFERENCE output = self.make_inference(request) # Monitoring service_id = request.service_id if self.predicted_class_kpi_id is None: self.predicted_class_kpi_id = self.create_predicted_class_kpi(self.monitoring_client, service_id) if self.class_probability_kpi_id is None: self.class_probability_kpi_id = self.create_class_prob_kpi(self.monitoring_client, service_id) # Packet -> DAD -> CAD -> ML -> (2 Instantaneous Value: higher class probability, predicted class) -> Monitoring # In addition, two counters: # Counter 1: Total number of crypto attack connections # Counter 2: Rate of crypto attack connections with respect to the total number of connections kpi_class = Kpi() kpi_class.kpi_id.kpi_id.uuid = self.predicted_class_kpi_id.uuid kpi_class.kpi_value.int32Val = 1 if request.tag_name == "Crypto" else 0 kpi_prob = Kpi() kpi_prob.kpi_id.kpi_id.uuid = self.class_probability_kpi_id.uuid kpi_prob.kpi_value.floatVal = request.confidence kpi_class.timestamp = kpi_prob.timestamp = timestamp_utcnow_to_float() self.monitoring_client.IncludeKpi(kpi_class) self.monitoring_client.IncludeKpi(kpi_prob) if output.tag_name == "Crypto": # SEND INFO TO MITIGATION SERVER try: with grpc.insecure_channel("192.168.165.78:10002") as channel: stub = L3AttackmitigatorStub(channel) print("Sending to mitigator...", flush=True) response = stub.SendOutput(output) # print("Response received", response, "Hola", flush=True) # print("Sent output to mitigator and received: ", response.message) #FIX No message received # RETURN "OK" TO THE CALLER return Empty(message="OK, information received and mitigator notified abou the attack") except Exception as e: print("This is an exception", repr(e), flush=True) print("Couldnt find l3_attackmitigator", flush=True) return Empty(message="Mitigator Not found") else: print("No attack detected", flush=True) return Empty(message="OK, information received (no attack detected)") def GetOutput(self, request, context): logging.debug("") print("Returing inference output...") k = np.multiply(self.inference_values, [2]) k = np.sum(k) return self.make_inference(k)