l3_centralizedattackdetectorServiceServicerImpl.py

# 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
from datetime import timedelta

import os
import numpy as np
import onnxruntime as rt
import onnx as ox
import logging
import time

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.monitoring_pb2 import KpiDescriptor
from common.proto.kpi_sample_types_pb2 import KpiSampleType

from monitoring.client.MonitoringClient import MonitoringClient
from common.proto.monitoring_pb2 import Kpi

from common.tools.timestamp.Converters import timestamp_utcnow_to_float
from common.proto.context_pb2 import Timestamp, SliceId, ConnectionId

from l3_attackmitigator.client.l3_attackmitigatorClient import l3_attackmitigatorClient

import uuid


LOGGER = logging.getLogger(__name__)
current_dir = os.path.dirname(os.path.abspath(__file__))
MODEL_FILE = os.path.join(current_dir, "ml_model/crypto_5g_rf_spider_features.onnx")


class l3_centralizedattackdetectorServiceServicerImpl(L3CentralizedattackdetectorServicer):

    """
    Initialize variables, prediction model and clients of components used by CAD
    """

    def __init__(self):
        LOGGER.info("Creating Centralized Attack Detector Service")

        onnx_model = ox.load_model(MODEL_FILE)
        meta = onnx_model.metadata_props.add()
        meta.key = "key"
        meta.value = "value"
        LOGGER.debug(onnx_model.metadata_props[0])

        self.inference_values = []
        self.inference_results = []
        self.model = rt.InferenceSession(MODEL_FILE)
        
        '''self.model._model_meta = metadata_proto
        meta = self.model.get_modelmeta()
        LOGGER.debug(meta.description)
        time.sleep(10)'''
        
        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.service_ids = []
        self.monitored_kpis = {
            "l3_security_status": {
                "kpi_id": None,
                "description": "L3 - Confidence of the cryptomining detector in the security status in the last time interval of the service {service_id}",
                "kpi_sample_type": KpiSampleType.KPISAMPLETYPE_L3_SECURITY_STATUS_CRYPTO,
                "service_ids": [],
            },
            "l3_ml_model_confidence": {
                "kpi_id": None,
                "description": "L3 - Security status of the service in a time interval of the service {service_id} (“0” if no attack has been detected on the service and “1” if a cryptomining attack has been detected)",
                "kpi_sample_type": KpiSampleType.KPISAMPLETYPE_ML_CONFIDENCE,
                "service_ids": [],
            },
            "l3_unique_attack_conns": {
                "kpi_id": None,
                "description": "L3 - Number of attack connections detected in a time interval of the service {service_id} (attacks of the same connection [origin IP, origin port, destination IP and destination port] are only considered once)",
                "kpi_sample_type": KpiSampleType.KPISAMPLETYPE_L3_UNIQUE_ATTACK_CONNS,
                "service_ids": [],
            },
            "l3_unique_compromised_clients": {
                "kpi_id": None,
                "description": "L3 - Number of unique compromised clients of the service in a time interval of the service {service_id} (attacks from the same origin IP are only considered once)",
                "kpi_sample_type": KpiSampleType.KPISAMPLETYPE_L3_UNIQUE_COMPROMISED_CLIENTS,
                "service_ids": [],
            },
            "l3_unique_attackers": {
                "kpi_id": None,
                "description": "L3 - number of unique attackers of the service in a time interval of the service {service_id} (attacks from the same destination IP are only considered once)",
                "kpi_sample_type": KpiSampleType.KPISAMPLETYPE_L3_UNIQUE_ATTACKERS,
                "service_ids": [],
            },
        }
        self.attackmitigator_client = l3_attackmitigatorClient()

        # Environment variables
        self.CLASSIFICATION_THRESHOLD = os.getenv("CAD_CLASSIFICATION_THRESHOLD", 0.5)
        self.MONITORED_KPIS_TIME_INTERVAL_AGG = os.getenv("MONITORED_KPIS_TIME_INTERVAL_AGG", 5)

        # Constants
        self.NORMAL_CLASS = 0
        self.CRYPTO_CLASS = 1

        self.kpi_test = None
        self.time_interval_start = None
        self.time_interval_end = None

        # CAD evaluation tests
        self.cad_inference_times = []
        self.cad_num_inference_measurements = 100

        # AM evaluation tests
        self.am_notification_times = []

    """
    Create a monitored KPI for a specific service and add it to the Monitoring Client
        -input: 
            + service_id: service ID where the KPI will be monitored
            + kpi_name: name of the KPI
            + kpi_description: description of the KPI
            + kpi_sample_type: KPI sample type of the KPI (it must be defined in the kpi_sample_types.proto file)
        -output: KPI identifier representing the KPI
    """

    def create_kpi(
        self,
        service_id,
        kpi_name,
        kpi_description,
        kpi_sample_type,
    ):
        kpidescriptor = KpiDescriptor()
        kpidescriptor.kpi_description = kpi_description
        kpidescriptor.service_id.service_uuid.uuid = service_id.service_uuid.uuid
        kpidescriptor.kpi_sample_type = kpi_sample_type
        new_kpi = self.monitoring_client.SetKpi(kpidescriptor)

        LOGGER.info("Created KPI {}".format(kpi_name))

        return new_kpi

    """
    Create the monitored KPIs for a specific service, add them to the Monitoring Client and store their identifiers in the monitored_kpis dictionary
        -input:
            + service_id: service ID where the KPIs will be monitored
        -output: None
    """

    def create_kpis(self, service_id, device_id, endpoint_id):
        LOGGER.info("Creating KPIs for service {}".format(service_id))

        # for now, all the KPIs are created for all the services from which requests are received
        for kpi in self.monitored_kpis:
            # generate random slice_id
            slice_id = SliceId()
            slice_id.slice_uuid.uuid = str(uuid.uuid4())

            # generate random connection_id
            connection_id = ConnectionId()
            connection_id.connection_uuid.uuid = str(uuid.uuid4())

            created_kpi = self.create_kpi(
                service_id,
                kpi,
                self.monitored_kpis[kpi]["description"].format(service_id=service_id.service_uuid.uuid),
                self.monitored_kpis[kpi]["kpi_sample_type"],
            )
            self.monitored_kpis[kpi]["kpi_id"] = created_kpi.kpi_id
            self.monitored_kpis[kpi]["service_ids"].append(service_id.service_uuid.uuid)

        LOGGER.info("Created KPIs for service {}".format(service_id))

    def monitor_kpis(
        self,
    ):
        monitor_inference_results = self.inference_results
        monitor_service_ids = self.service_ids

        LOGGER.debug("monitor_inference_results: {}".format(len(monitor_inference_results)))
        LOGGER.debug("monitor_service_ids: {}".format(len(monitor_service_ids)))

        time_interval = self.MONITORED_KPIS_TIME_INTERVAL_AGG

        # assign the timestamp of the first inference result to the time_interval_start
        if self.time_interval_start is None:
            self.time_interval_start = monitor_inference_results[0]["timestamp"]
            LOGGER.debug("self.time_interval_start: {}".format(self.time_interval_start))
            # self.time_interval_start = datetime.strptime(self.time_interval_start, "%Y-%m-%d %H:%M:%S.%f")

            # add time_interval to the current time to get the time interval end
            LOGGER.debug("time_interval: {}".format(time_interval))
            LOGGER.debug(timedelta(seconds=time_interval))
            self.time_interval_end = self.time_interval_start + timedelta(seconds=time_interval)

        current_time = datetime.utcnow()

        LOGGER.debug("current_time: {}".format(current_time))

        if current_time >= self.time_interval_end:
            self.time_interval_start = self.time_interval_end
            self.time_interval_end = self.time_interval_start + timedelta(seconds=time_interval)

        LOGGER.debug("time_interval_start: {}".format(self.time_interval_start))
        LOGGER.debug("time_interval_end: {}".format(self.time_interval_end))

        # delete all inference results that are older than the time_interval_start
        delete_inference_results = []

        for i in range(len(monitor_inference_results)):
            inference_result_timestamp = monitor_inference_results[i]["timestamp"]

            if inference_result_timestamp < self.time_interval_start:
                delete_inference_results.append(monitor_inference_results[i])

        if len(delete_inference_results) > 0:
            monitor_inference_results = [
                inference_result
                for inference_result in monitor_inference_results
                if inference_result not in delete_inference_results
            ]
            LOGGER.debug(f"Cleaned inference results. {len(delete_inference_results)} inference results deleted")

        # check if there is at least one inference result in monitor_inference_results in the current time_interval
        num_inference_results_in_time_interval = 0

        for i in range(len(monitor_inference_results)):
            inference_result_timestamp = monitor_inference_results[i]["timestamp"]

            if (
                inference_result_timestamp >= self.time_interval_start
                and inference_result_timestamp < self.time_interval_end
            ):
                num_inference_results_in_time_interval += 1

        if num_inference_results_in_time_interval > 0:
            non_empty_time_interval = True
            LOGGER.debug(
                f"Current time interval is not empty (there are {num_inference_results_in_time_interval} inference results"
            )
        else:
            non_empty_time_interval = False
            LOGGER.debug("Current time interval is empty. No KPIs will be reported.")

        if non_empty_time_interval:
            for service_id in monitor_service_ids:
                LOGGER.debug("service_id: {}".format(service_id))

                # L3 security status
                kpi_security_status = Kpi()
                kpi_security_status.kpi_id.kpi_id.CopyFrom(self.monitored_kpis["l3_security_status"]["kpi_id"])

                # get the output.tag of the ML model of the last aggregation time interval as indicated by the self.MONITORED_KPIS_TIME_INTERVAL_AGG variable
                outputs_last_time_interval = []

                for i in range(len(monitor_inference_results)):
                    if (
                        monitor_inference_results[i]["timestamp"] >= self.time_interval_start
                        and monitor_inference_results[i]["timestamp"] < self.time_interval_end
                        and monitor_inference_results[i]["output"]["service_id"] == service_id
                        and service_id.service_uuid.uuid in self.monitored_kpis["l3_security_status"]["service_ids"]
                    ):
                        outputs_last_time_interval.append(monitor_inference_results[i]["output"]["tag"])

                LOGGER.debug("outputs_last_time_interval: {}".format(outputs_last_time_interval))

                # check if all outputs are 0
                all_outputs_zero = True
                for output in outputs_last_time_interval:
                    if output != self.NORMAL_CLASS:
                        all_outputs_zero = False
                        break

                if all_outputs_zero:
                    kpi_security_status.kpi_value.int32Val = 0
                else:
                    kpi_security_status.kpi_value.int32Val = 1

                # L3 ML model confidence
                kpi_conf = Kpi()
                kpi_conf.kpi_id.kpi_id.CopyFrom(self.monitored_kpis["l3_ml_model_confidence"]["kpi_id"])

                # get the output.confidence of the ML model of the last aggregation time interval as indicated by the self.MONITORED_KPIS_TIME_INTERVAL_AGG variable
                confidences_normal_last_time_interval = []
                confidences_crypto_last_time_interval = []

                for i in range(len(monitor_inference_results)):
                    LOGGER.debug("monitor_inference_results[i]: {}".format(monitor_inference_results[i]))

                    if (
                        monitor_inference_results[i]["timestamp"] >= self.time_interval_start
                        and monitor_inference_results[i]["timestamp"] < self.time_interval_end
                        and monitor_inference_results[i]["output"]["service_id"] == service_id
                        and service_id.service_uuid.uuid
                        in self.monitored_kpis["l3_ml_model_confidence"]["service_ids"]
                    ):
                        if monitor_inference_results[i]["output"]["tag"] == self.NORMAL_CLASS:
                            confidences_normal_last_time_interval.append(
                                monitor_inference_results[i]["output"]["confidence"]
                            )
                        elif monitor_inference_results[i]["output"]["tag"] == self.CRYPTO_CLASS:
                            confidences_crypto_last_time_interval.append(
                                monitor_inference_results[i]["output"]["confidence"]
                            )
                        else:
                            LOGGER.debug("Unknown tag: {}".format(monitor_inference_results[i]["output"]["tag"]))

                LOGGER.debug("confidences_normal_last_time_interval: {}".format(confidences_normal_last_time_interval))
                LOGGER.debug("confidences_crypto_last_time_interval: {}".format(confidences_crypto_last_time_interval))

                if kpi_security_status.kpi_value.int32Val == 0:
                    kpi_conf.kpi_value.floatVal = np.mean(confidences_normal_last_time_interval)
                else:
                    kpi_conf.kpi_value.floatVal = np.mean(confidences_crypto_last_time_interval)

                # L3 unique attack connections
                kpi_unique_attack_conns = Kpi()
                kpi_unique_attack_conns.kpi_id.kpi_id.CopyFrom(self.monitored_kpis["l3_unique_attack_conns"]["kpi_id"])

                # get the number of unique attack connections (grouping by origin IP, origin port, destination IP, destination port) of the last aggregation time interval as indicated by the self.MONITORED_KPIS_TIME_INTERVAL_AGG variable
                num_unique_attack_conns_last_time_interval = 0
                unique_attack_conns_last_time_interval = []

                for i in range(len(monitor_inference_results)):
                    if (
                        monitor_inference_results[i]["timestamp"] >= self.time_interval_start
                        and monitor_inference_results[i]["timestamp"] < self.time_interval_end
                        and monitor_inference_results[i]["output"]["service_id"] == service_id
                        and service_id.service_uuid.uuid
                        in self.monitored_kpis["l3_unique_attack_conns"]["service_ids"]
                    ):
                        if monitor_inference_results[i]["output"]["tag"] == self.CRYPTO_CLASS:
                            current_attack_conn = {
                                "ip_o": monitor_inference_results[i]["output"]["ip_o"],
                                "port_o": monitor_inference_results[i]["output"]["port_o"],
                                "ip_d": monitor_inference_results[i]["output"]["ip_d"],
                                "port_d": monitor_inference_results[i]["output"]["port_d"],
                            }

                            is_unique_attack_conn = True

                            for j in range(len(unique_attack_conns_last_time_interval)):
                                if current_attack_conn == unique_attack_conns_last_time_interval[j]:
                                    is_unique_attack_conn = False

                            if is_unique_attack_conn:
                                num_unique_attack_conns_last_time_interval += 1
                                unique_attack_conns_last_time_interval.append(current_attack_conn)

                kpi_unique_attack_conns.kpi_value.int32Val = num_unique_attack_conns_last_time_interval

                # L3 unique compromised clients
                kpi_unique_compromised_clients = Kpi()
                kpi_unique_compromised_clients.kpi_id.kpi_id.CopyFrom(
                    self.monitored_kpis["l3_unique_compromised_clients"]["kpi_id"]
                )

                # get the number of unique compromised clients (grouping by origin IP) of the last aggregation time interval as indicated by the self.MONITORED_KPIS_TIME_INTERVAL_AGG variable
                num_unique_compromised_clients_last_time_interval = 0
                unique_compromised_clients_last_time_interval = []

                for i in range(len(monitor_inference_results)):
                    if (
                        monitor_inference_results[i]["timestamp"] >= self.time_interval_start
                        and monitor_inference_results[i]["timestamp"] < self.time_interval_end
                        and monitor_inference_results[i]["output"]["service_id"] == service_id
                        and service_id.service_uuid.uuid
                        in self.monitored_kpis["l3_unique_compromised_clients"]["service_ids"]
                    ):
                        if monitor_inference_results[i]["output"]["tag"] == self.CRYPTO_CLASS:
                            if (
                                monitor_inference_results[i]["output"]["ip_o"]
                                not in unique_compromised_clients_last_time_interval
                            ):
                                unique_compromised_clients_last_time_interval.append(
                                    monitor_inference_results[i]["output"]["ip_o"]
                                )
                                num_unique_compromised_clients_last_time_interval += 1

                kpi_unique_compromised_clients.kpi_value.int32Val = num_unique_compromised_clients_last_time_interval

                # L3 unique attackers
                kpi_unique_attackers = Kpi()
                kpi_unique_attackers.kpi_id.kpi_id.CopyFrom(self.monitored_kpis["l3_unique_attackers"]["kpi_id"])

                # get the number of unique attackers (grouping by destination ip) of the last aggregation time interval as indicated by the self.MONITORED_KPIS_TIME_INTERVAL_AGG variable
                num_unique_attackers_last_time_interval = 0
                unique_attackers_last_time_interval = []

                for i in range(len(monitor_inference_results)):
                    if (
                        monitor_inference_results[i]["timestamp"] >= self.time_interval_start
                        and monitor_inference_results[i]["timestamp"] < self.time_interval_end
                        and monitor_inference_results[i]["output"]["service_id"] == service_id
                        and service_id.service_uuid.uuid in self.monitored_kpis["l3_unique_attackers"]["service_ids"]
                    ):
                        if monitor_inference_results[i]["output"]["tag"] == self.CRYPTO_CLASS:
                            if (
                                monitor_inference_results[i]["output"]["ip_d"]
                                not in unique_attackers_last_time_interval
                            ):
                                unique_attackers_last_time_interval.append(
                                    monitor_inference_results[i]["output"]["ip_d"]
                                )
                                num_unique_attackers_last_time_interval += 1

                kpi_unique_attackers.kpi_value.int32Val = num_unique_attackers_last_time_interval

                timestamp = Timestamp()
                timestamp.timestamp = timestamp_utcnow_to_float()

                kpi_security_status.timestamp.CopyFrom(timestamp)
                kpi_conf.timestamp.CopyFrom(timestamp)
                kpi_unique_attack_conns.timestamp.CopyFrom(timestamp)
                kpi_unique_compromised_clients.timestamp.CopyFrom(timestamp)
                kpi_unique_attackers.timestamp.CopyFrom(timestamp)

                LOGGER.debug("Sending KPIs to monitoring server")

                LOGGER.debug("kpi_security_status: {}".format(kpi_security_status))
                LOGGER.debug("kpi_conf: {}".format(kpi_conf))
                LOGGER.debug("kpi_unique_attack_conns: {}".format(kpi_unique_attack_conns))
                LOGGER.debug("kpi_unique_compromised_clients: {}".format(kpi_unique_compromised_clients))
                LOGGER.debug("kpi_unique_attackers: {}".format(kpi_unique_attackers))

                try:
                    self.monitoring_client.IncludeKpi(kpi_security_status)
                    self.monitoring_client.IncludeKpi(kpi_conf)
                    self.monitoring_client.IncludeKpi(kpi_unique_attack_conns)
                    self.monitoring_client.IncludeKpi(kpi_unique_compromised_clients)
                    self.monitoring_client.IncludeKpi(kpi_unique_attackers)
                except Exception as e:
                    LOGGER.debug("Error sending KPIs to monitoring server: {}".format(e))

                LOGGER.debug("KPIs sent to monitoring server")
        else:
            LOGGER.debug("No KPIs sent to monitoring server")

    """
    Classify connection as standard traffic or cryptomining attack and return results
        -input: 
            + request: L3CentralizedattackdetectorMetrics object with connection features information
        -output: L3AttackmitigatorOutput object with information about the assigned class and prediction confidence
    """

    def make_inference(self, request):
        '''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,
                ]
            ]
        )'''
        
        x_data = np.array(
            [
                [feature.feature for feature in request.features]
            ]
        )

        # Print input data shape
        LOGGER.debug("x_data.shape: {}".format(x_data.shape))

        # Get batch size
        batch_size = x_data.shape[0]

        # Print batch size
        LOGGER.debug("batch_size: {}".format(batch_size))

        # TEST: Remove later
        test_batch_size = 1024
        # duplicate x_data to test_batch_size
        x_data = np.repeat(x_data, test_batch_size, axis=0)

        LOGGER.debug("x_data.shape: {}".format(x_data.shape))

        inference_time_start = time.perf_counter()

        # Perform inference
        predictions = self.model.run([self.prob_name], {self.input_name: x_data.astype(np.float32)})[0]

        inference_time_end = time.perf_counter()

        # Measure inference time
        inference_time = inference_time_end - inference_time_start
        self.cad_inference_times.append(inference_time)

        if len(self.cad_inference_times) > self.cad_num_inference_measurements:
            inference_times_np_array = np.array(self.cad_inference_times)
            np.save(f"inference_times_{test_batch_size}.npy", inference_times_np_array)

            avg_inference_time = np.mean(inference_times_np_array)
            max_inference_time = np.max(inference_times_np_array)
            min_inference_time = np.min(inference_times_np_array)
            std_inference_time = np.std(inference_times_np_array)
            median_inference_time = np.median(inference_times_np_array)

            LOGGER.debug("Average inference time: {}".format(avg_inference_time))
            LOGGER.debug("Max inference time: {}".format(max_inference_time))
            LOGGER.debug("Min inference time: {}".format(min_inference_time))
            LOGGER.debug("Standard deviation inference time: {}".format(std_inference_time))
            LOGGER.debug("Median inference time: {}".format(median_inference_time))

            with open(f"inference_times_stats_{batch_size}.txt", "w") as f:
                f.write("Average inference time: {}\n".format(avg_inference_time))
                f.write("Max inference time: {}\n".format(max_inference_time))
                f.write("Min inference time: {}\n".format(min_inference_time))
                f.write("Standard deviation inference time: {}\n".format(std_inference_time))
                f.write("Median inference time: {}\n".format(median_inference_time))

        # Gather the predicted class, the probability of that class and other relevant information required to block the attack
        output_message = {
            "confidence": None,
            "timestamp": datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
            "ip_o": request.connection_metadata.ip_o,
            "ip_d": request.connection_metadata.ip_d,
            "tag_name": None,
            "tag": None,
            "flow_id": request.connection_metadata.flow_id,
            "protocol": request.connection_metadata.protocol,
            "port_o": request.connection_metadata.port_o,
            "port_d": request.connection_metadata.port_d,
            "ml_id": "RandomForest",
            "service_id": request.connection_metadata.service_id,
            "endpoint_id": request.connection_metadata.endpoint_id,
            "time_start": request.connection_metadata.time_start,
            "time_end": request.connection_metadata.time_end,
        }

        if predictions[0][1] >= self.CLASSIFICATION_THRESHOLD:
            output_message["confidence"] = predictions[0][1]
            output_message["tag_name"] = "Crypto"
            output_message["tag"] = self.CRYPTO_CLASS
        else:
            output_message["confidence"] = predictions[0][0]
            output_message["tag_name"] = "Normal"
            output_message["tag"] = self.NORMAL_CLASS

        return output_message

    """
    Receive features from Attack Mitigator, predict attack and communicate with Attack Mitigator
        -input: 
            + request: L3CentralizedattackdetectorMetrics object with connection features information
        -output: Empty object with a message about the execution of the function
    """

    def SendInput(self, request, context):
        # Perform inference with the data sent in the request
        logging.info("Performing inference...")
        cryptomining_detector_output = self.make_inference(request)
        logging.info("Inference performed correctly")

        self.inference_results.append({"output": cryptomining_detector_output, "timestamp": datetime.now()})

        service_id = request.connection_metadata.service_id
        device_id = request.connection_metadata.endpoint_id.device_id
        endpoint_id = request.connection_metadata.endpoint_id

        # Check if a request of a new service has been received and, if so, create the monitored KPIs for that service
        if service_id not in self.service_ids:
            self.create_kpis(service_id, device_id, endpoint_id)
            self.service_ids.append(service_id)

        self.monitor_kpis()

        LOGGER.debug("cryptomining_detector_output: {}".format(cryptomining_detector_output))

        # Only notify Attack Mitigator when a cryptomining connection has been detected
        if cryptomining_detector_output["tag_name"] == "Crypto":
            notification_time_start = time.perf_counter()

            logging.info("Crypto attack detected")

            # Notify the Attack Mitigator component about the attack
            logging.info(
                "Notifying the Attack Mitigator component about the attack in order to block the connection..."
            )

            try:
                logging.info("Sending the connection information to the Attack Mitigator component...")
                message = L3AttackmitigatorOutput(**cryptomining_detector_output)
                response = self.attackmitigator_client.SendOutput(message)
                notification_time_end = time.perf_counter()

                self.am_notification_times.append(notification_time_end - notification_time_start)

                LOGGER.debug(f"am_notification_times length: {len(self.am_notification_times)}")

                if len(self.am_notification_times) > 100:
                    am_notification_times_np_array = np.array(self.am_notification_times)
                    np.save("am_notification_times.npy", am_notification_times_np_array)

                    avg_notification_time = np.mean(am_notification_times_np_array)
                    max_notification_time = np.max(am_notification_times_np_array)
                    min_notification_time = np.min(am_notification_times_np_array)
                    std_notification_time = np.std(am_notification_times_np_array)
                    median_notification_time = np.median(am_notification_times_np_array)

                    LOGGER.debug("Average notification time: {}".format(avg_notification_time))
                    LOGGER.debug("Max notification time: {}".format(max_notification_time))
                    LOGGER.debug("Min notification time: {}".format(min_notification_time))
                    LOGGER.debug("Std notification time: {}".format(std_notification_time))
                    LOGGER.debug("Median notification time: {}".format(median_notification_time))

                    with open("am_notification_times_stats.txt", "w") as f:
                        f.write("Average notification time: {}\n".format(avg_notification_time))
                        f.write("Max notification time: {}\n".format(max_notification_time))
                        f.write("Min notification time: {}\n".format(min_notification_time))
                        f.write("Std notification time: {}\n".format(std_notification_time))
                        f.write("Median notification time: {}\n".format(median_notification_time))

                # logging.info("Attack Mitigator notified and received response: ", response.message)  # FIX No message received
                logging.info("Attack Mitigator notified")

                return Empty(message="OK, information received and mitigator notified abou the attack")
            except Exception as e:
                logging.error("Error notifying the Attack Mitigator component about the attack: ", e)
                logging.error("Couldn't find l3_attackmitigator")

                return Empty(message="Attack Mitigator not found")
        else:
            logging.info("No attack detected")

            return Empty(message="Ok, information received (no attack detected)")