Merge branch 'dev' into feature/1386-enable-a-repeatable-or-deterministic-baseline-test

.azure/azure-ci-build-pipeline.yaml

@@ -25,6 +25,12 @@ steps:
     versionSpec: '$(python.version)'
   displayName: 'Use Python $(python.version)'
 
+- script: |
+    python -m pip install pre-commit
+    pre-commit install
+    pre-commit run --all-files
+  displayName: 'Run pre-commits'
+
 - script: |
     python -m pip install --upgrade pip==23.0.1
     pip install wheel==0.38.4 --upgrade

docs/source/config.rst

@@ -28,6 +28,10 @@ The environment config file consists of the following attributes:
    * STABLE_BASELINES3_PPO - Use a SB3 PPO agent
    * STABLE_BASELINES3_A2C - use a SB3 A2C agent
 
+* **random_red_agent** [bool]
+
+    Determines if the session should be run with a random red agent
+
 * **action_type** [enum]
 
    Determines whether a NODE, ACL, or ANY (combined NODE & ACL) action space format is adopted for the session

docs/source/primaite_session.rst

@@ -78,10 +78,9 @@ PrimAITE automatically creates two sets of results from each session:
     * Timestamp
     * Episode number
     * Step number
-    * Initial observation space (before red and blue agent actions have been taken). Individual elements of the observation space are presented in the format OSI_X_Y
+    * Initial observation space (what the blue agent observed when it decided its action)
-    * Resulting observation space (after the red and blue agent actions have been taken) Individual elements of the observation space are presented in the format OSN_X_Y
     * Reward value
-    * Action space (as presented by the blue agent on this step). Individual elements of the action space are presented in the format AS_X
+    * Action taken (as presented by the blue agent on this step). Individual elements of the action space are presented in the format AS_X
 
 **Diagrams**
 

src/primaite/config/_package_data/training/training_config_main.yaml

@@ -6,11 +6,23 @@
 # "STABLE_BASELINES3_A2C"
 # "GENERIC"
 agent_identifier: STABLE_BASELINES3_A2C
+
+# RED AGENT IDENTIFIER
+# RANDOM or NONE
+random_red_agent: False
+
 # Sets How the Action Space is defined:
 # "NODE"
 # "ACL"
 # "ANY" node and acl actions
 action_type: NODE
+# observation space
+observation_space:
+  # flatten: true
+  components:
+    - name: NODE_LINK_TABLE
+    # - name: NODE_STATUSES
+    # - name: LINK_TRAFFIC_LEVELS
 # Number of episodes to run per session
 num_episodes: 10
 # Number of time_steps per episode

src/primaite/config/_package_data/training/training_config_random_red_agent.yaml

@@ -0,0 +1,99 @@
+# Main Config File
+
+# Generic config values
+# Choose one of these (dependent on Agent being trained)
+# "STABLE_BASELINES3_PPO"
+# "STABLE_BASELINES3_A2C"
+# "GENERIC"
+agent_identifier: STABLE_BASELINES3_A2C
+
+# RED AGENT IDENTIFIER
+# RANDOM or NONE
+random_red_agent: True
+
+# Sets How the Action Space is defined:
+# "NODE"
+# "ACL"
+# "ANY" node and acl actions
+action_type: NODE
+# Number of episodes to run per session
+num_episodes: 10
+# Number of time_steps per episode
+num_steps: 256
+# Time delay between steps (for generic agents)
+time_delay: 10
+# Type of session to be run (TRAINING or EVALUATION)
+session_type: TRAINING
+# Determine whether to load an agent from file
+load_agent: False
+# File path and file name of agent if you're loading one in
+agent_load_file: C:\[Path]\[agent_saved_filename.zip]
+
+# Environment config values
+# The high value for the observation space
+observation_space_high_value: 1000000000
+
+# Reward values
+# Generic
+all_ok: 0
+# Node Hardware State
+off_should_be_on: -10
+off_should_be_resetting: -5
+on_should_be_off: -2
+on_should_be_resetting: -5
+resetting_should_be_on: -5
+resetting_should_be_off: -2
+resetting: -3
+# Node Software or Service State
+good_should_be_patching: 2
+good_should_be_compromised: 5
+good_should_be_overwhelmed: 5
+patching_should_be_good: -5
+patching_should_be_compromised: 2
+patching_should_be_overwhelmed: 2
+patching: -3
+compromised_should_be_good: -20
+compromised_should_be_patching: -20
+compromised_should_be_overwhelmed: -20
+compromised: -20
+overwhelmed_should_be_good: -20
+overwhelmed_should_be_patching: -20
+overwhelmed_should_be_compromised: -20
+overwhelmed: -20
+# Node File System State
+good_should_be_repairing: 2
+good_should_be_restoring: 2
+good_should_be_corrupt: 5
+good_should_be_destroyed: 10
+repairing_should_be_good: -5
+repairing_should_be_restoring: 2
+repairing_should_be_corrupt: 2
+repairing_should_be_destroyed: 0
+repairing: -3
+restoring_should_be_good: -10
+restoring_should_be_repairing: -2
+restoring_should_be_corrupt: 1
+restoring_should_be_destroyed: 2
+restoring: -6
+corrupt_should_be_good: -10
+corrupt_should_be_repairing: -10
+corrupt_should_be_restoring: -10
+corrupt_should_be_destroyed: 2
+corrupt: -10
+destroyed_should_be_good: -20
+destroyed_should_be_repairing: -20
+destroyed_should_be_restoring: -20
+destroyed_should_be_corrupt: -20
+destroyed: -20
+scanning: -2
+# IER status
+red_ier_running: -5
+green_ier_blocked: -10
+
+# Patching / Reset durations
+os_patching_duration: 5            # The time taken to patch the OS
+node_reset_duration: 5             # The time taken to reset a node (hardware)
+service_patching_duration: 5       # The time taken to patch a service
+file_system_repairing_limit: 5      # The time take to repair the file system
+file_system_restoring_limit: 5      # The time take to restore the file system
+file_system_scanning_limit: 5       # The time taken to scan the file system

src/primaite/config/training_config.py

@@ -21,6 +21,9 @@ class TrainingConfig:
     agent_identifier: str = "STABLE_BASELINES3_A2C"
     "The Red Agent algo/class to be used."
 
+    random_red_agent: bool = False
+    "Creates Random Red Agent Attacks"
+
     action_type: ActionType = ActionType.ANY
     "The ActionType to use."
 

src/primaite/environment/observations.py

@@ -29,6 +29,7 @@ class AbstractObservationComponent(ABC):
         self.env: "Primaite" = env
         self.space: spaces.Space
         self.current_observation: np.ndarray  # type might be too restrictive?
+        self.structure: List[str]
         return NotImplemented
 
     @abstractmethod
@@ -36,6 +37,11 @@ class AbstractObservationComponent(ABC):
         """Update the observation based on the current state of the environment."""
         self.current_observation = NotImplemented
 
+    @abstractmethod
+    def generate_structure(self) -> List[str]:
+        """Return a list of labels for the components of the flattened observation space."""
+        return NotImplemented
+
 
 class NodeLinkTable(AbstractObservationComponent):
     """Table with nodes and links as rows and hardware/software status as cols.
@@ -79,6 +85,8 @@ class NodeLinkTable(AbstractObservationComponent):
         # 3. Initialise Observation with zeroes
         self.current_observation = np.zeros(observation_shape, dtype=self._DATA_TYPE)
 
+        self.structure = self.generate_structure()
+
     def update(self):
         """Update the observation based on current environment state.
 
@@ -131,6 +139,40 @@ class NodeLinkTable(AbstractObservationComponent):
                 protocol_index += 1
             item_index += 1
 
+    def generate_structure(self):
+        """Return a list of labels for the components of the flattened observation space."""
+        nodes = self.env.nodes.values()
+        links = self.env.links.values()
+
+        structure = []
+
+        for i, node in enumerate(nodes):
+            node_id = node.node_id
+            node_labels = [
+                f"node_{node_id}_id",
+                f"node_{node_id}_hardware_status",
+                f"node_{node_id}_os_status",
+                f"node_{node_id}_fs_status",
+            ]
+            for j, serv in enumerate(self.env.services_list):
+                node_labels.append(f"node_{node_id}_service_{serv}_status")
+
+            structure.extend(node_labels)
+
+        for i, link in enumerate(links):
+            link_id = link.id
+            link_labels = [
+                f"link_{link_id}_id",
+                f"link_{link_id}_n/a",
+                f"link_{link_id}_n/a",
+                f"link_{link_id}_n/a",
+            ]
+            for j, serv in enumerate(self.env.services_list):
+                link_labels.append(f"link_{link_id}_service_{serv}_load")
+
+            structure.extend(link_labels)
+        return structure
+
 
 class NodeStatuses(AbstractObservationComponent):
     """Flat list of nodes' hardware, OS, file system, and service states.
@@ -179,6 +221,7 @@ class NodeStatuses(AbstractObservationComponent):
 
         # 3. Initialise observation with zeroes
         self.current_observation = np.zeros(len(shape), dtype=self._DATA_TYPE)
+        self.structure = self.generate_structure()
 
     def update(self):
         """Update the observation based on current environment state.
@@ -205,6 +248,30 @@ class NodeStatuses(AbstractObservationComponent):
             )
         self.current_observation[:] = obs
 
+    def generate_structure(self):
+        """Return a list of labels for the components of the flattened observation space."""
+        services = self.env.services_list
+
+        structure = []
+        for _, node in self.env.nodes.items():
+            node_id = node.node_id
+            structure.append(f"node_{node_id}_hardware_state_NONE")
+            for state in HardwareState:
+                structure.append(f"node_{node_id}_hardware_state_{state.name}")
+            structure.append(f"node_{node_id}_software_state_NONE")
+            for state in SoftwareState:
+                structure.append(f"node_{node_id}_software_state_{state.name}")
+            structure.append(f"node_{node_id}_file_system_state_NONE")
+            for state in FileSystemState:
+                structure.append(f"node_{node_id}_file_system_state_{state.name}")
+            for service in services:
+                structure.append(f"node_{node_id}_service_{service}_state_NONE")
+                for state in SoftwareState:
+                    structure.append(
+                        f"node_{node_id}_service_{service}_state_{state.name}"
+                    )
+        return structure
+
 
 class LinkTrafficLevels(AbstractObservationComponent):
     """Flat list of traffic levels encoded into banded categories.
@@ -268,6 +335,8 @@ class LinkTrafficLevels(AbstractObservationComponent):
         # 3. Initialise observation with zeroes
         self.current_observation = np.zeros(len(shape), dtype=self._DATA_TYPE)
 
+        self.structure = self.generate_structure()
+
     def update(self):
         """Update the observation based on current environment state.
 
@@ -295,6 +364,21 @@ class LinkTrafficLevels(AbstractObservationComponent):
 
         self.current_observation[:] = obs
 
+    def generate_structure(self):
+        """Return a list of labels for the components of the flattened observation space."""
+        structure = []
+        for _, link in self.env.links.items():
+            link_id = link.id
+            if self._combine_service_traffic:
+                protocols = ["overall"]
+            else:
+                protocols = [protocol.name for protocol in link.protocol_list]
+
+            for p in protocols:
+                for i in range(self._quantisation_levels):
+                    structure.append(f"link_{link_id}_{p}_traffic_level_{i}")
+        return structure
+
 
 class ObservationsHandler:
     """Component-based observation space handler.
@@ -311,8 +395,17 @@ class ObservationsHandler:
 
     def __init__(self):
         self.registered_obs_components: List[AbstractObservationComponent] = []
-        self.space: spaces.Space
+
-        self.current_observation: Union[Tuple[np.ndarray], np.ndarray]
+        # internal the observation space (unflattened version of space if flatten=True)
+        self._space: spaces.Space
+        # flattened version of the observation space
+        self._flat_space: spaces.Space
+
+        self._observation: Union[Tuple[np.ndarray], np.ndarray]
+        # used for transactions and when flatten=true
+        self._flat_observation: np.ndarray
+
+        self.flatten: bool = False
 
     def update_obs(self):
         """Fetch fresh information about the environment."""
@@ -321,12 +414,11 @@ class ObservationsHandler:
             obs.update()
             current_obs.append(obs.current_observation)
 
-        # If there is only one component, don't use a tuple, just pass through that component's obs.
         if len(current_obs) == 1:
-            self.current_observation = current_obs[0]
+            self._observation = current_obs[0]
         else:
-            self.current_observation = tuple(current_obs)
+            self._observation = tuple(current_obs)
-            # TODO: We may need to add ability to flatten the space as not all agents support tuple spaces.
+        self._flat_observation = spaces.flatten(self._space, self._observation)
 
     def register(self, obs_component: AbstractObservationComponent):
         """Add a component for this handler to track.
@@ -353,12 +445,31 @@ class ObservationsHandler:
         for obs_comp in self.registered_obs_components:
             component_spaces.append(obs_comp.space)
 
-        # If there is only one component, don't use a tuple space, just pass through that component's space.
+        # if there are multiple components, build a composite tuple space
         if len(component_spaces) == 1:
-            self.space = component_spaces[0]
+            self._space = component_spaces[0]
         else:
-            self.space = spaces.Tuple(component_spaces)
+            self._space = spaces.Tuple(component_spaces)
-            # TODO: We may need to add ability to flatten the space as not all agents support tuple spaces.
+        if len(component_spaces) > 0:
+            self._flat_space = spaces.flatten_space(self._space)
+        else:
+            self._flat_space = spaces.Box(0, 1, (0,))
+
+    @property
+    def space(self):
+        """Observation space, return the flattened version if flatten is True."""
+        if self.flatten:
+            return self._flat_space
+        else:
+            return self._space
+
+    @property
+    def current_observation(self):
+        """Current observation, return the flattened version if flatten is True."""
+        if self.flatten:
+            return self._flat_observation
+        else:
+            return self._observation
 
     @classmethod
     def from_config(cls, env: "Primaite", obs_space_config: dict):
@@ -388,6 +499,9 @@ class ObservationsHandler:
         # Instantiate the handler
         handler = cls()
 
+        if obs_space_config.get("flatten"):
+            handler.flatten = True
+
         for component_cfg in obs_space_config["components"]:
             # Figure out which class can instantiate the desired component
             comp_type = component_cfg["name"]
@@ -401,3 +515,17 @@ class ObservationsHandler:
 
         handler.update_obs()
         return handler
+
+    def describe_structure(self):
+        """Create a list of names for the features of the obs space.
+
+        The order of labels follows the flattened version of the space.
+        """
+        # as it turns out it's not possible to take the gym flattening function and apply it to our labels so we have
+        # to fake it. each component has to just hard-code the expected label order after flattening...
+
+        labels = []
+        for obs_comp in self.registered_obs_components:
+            labels.extend(obs_comp.structure)
+
+        return labels

src/primaite/environment/primaite_env.py

@@ -3,8 +3,10 @@
 import copy
 import csv
 import logging
+import uuid as uuid
 from datetime import datetime
 from pathlib import Path
+from random import choice, randint, sample, uniform
 from typing import Dict, Tuple, Union
 
 import networkx as nx
@@ -197,7 +199,6 @@ class Primaite(Env):
         try:
             plt.tight_layout()
             nx.draw_networkx(self.network, with_labels=True)
-            # now = datetime.now()  # current date and time
 
             file_path = session_path / f"network_{timestamp_str}.png"
             plt.savefig(file_path, format="PNG")
@@ -281,6 +282,10 @@ class Primaite(Env):
         # Does this for both live and reference nodes
         self.reset_environment()
 
+        # Create a random red agent to use for this episode
+        if self.training_config.random_red_agent:
+            self._create_random_red_agent()
+
         # Reset counters and totals
         self.total_reward = 0
         self.step_count = 0
@@ -325,7 +330,8 @@ class Primaite(Env):
             datetime.now(), self.agent_identifier, self.episode_count, self.step_count
         )
         # Load the initial observation space into the transaction
-        transaction.set_obs_space_pre(copy.deepcopy(self.env_obs))
+        transaction.set_obs_space(self.obs_handler._flat_observation)
+
         # Load the action space into the transaction
         transaction.set_action_space(copy.deepcopy(action))
 
@@ -406,8 +412,6 @@ class Primaite(Env):
 
         # 7. Update env_obs
         self.update_environent_obs()
-        # Load the new observation space into the transaction
-        transaction.set_obs_space_post(copy.deepcopy(self.env_obs))
 
         # 8. Add the transaction to the list of transactions
         self.transaction_list.append(copy.deepcopy(transaction))
@@ -1240,3 +1244,136 @@ class Primaite(Env):
         # Combine the Node dict and ACL dict
         combined_action_dict = {**acl_action_dict, **new_node_action_dict}
         return combined_action_dict
+
+    def _create_random_red_agent(self):
+        """Decide on random red agent for the episode to be called in env.reset()."""
+        # Reset the current red iers and red node pol
+        self.red_iers = {}
+        self.red_node_pol = {}
+
+        # Decide how many nodes become compromised
+        node_list = list(self.nodes.values())
+        computers = [node for node in node_list if node.node_type == NodeType.COMPUTER]
+        max_num_nodes_compromised = len(
+            computers
+        )  # only computers can become compromised
+        # random select between 1 and max_num_nodes_compromised
+        num_nodes_to_compromise = randint(1, max_num_nodes_compromised)
+
+        # Decide which of the nodes to compromise
+        nodes_to_be_compromised = sample(computers, num_nodes_to_compromise)
+
+        # choose a random compromise node to be source of attacks
+        source_node = choice(nodes_to_be_compromised)
+
+        # For each of the nodes to be compromised decide which step they become compromised
+        max_step_compromised = (
+            self.episode_steps // 2
+        )  # always compromise in first half of episode
+
+        # Bandwidth for all links
+        bandwidths = [i.get_bandwidth() for i in list(self.links.values())]
+
+        if len(bandwidths) < 1:
+            msg = "Random red agent cannot be used on a network without any links"
+            _LOGGER.error(msg)
+            raise Exception(msg)
+
+        servers = [node for node in node_list if node.node_type == NodeType.SERVER]
+
+        for n, node in enumerate(nodes_to_be_compromised):
+            # 1: Use Node PoL to set node to compromised
+
+            _id = str(uuid.uuid4())
+            _start_step = randint(2, max_step_compromised + 1)  # step compromised
+            pol_service_name = choice(list(node.services.keys()))
+
+            source_node_service = choice(list(source_node.services.values()))
+
+            red_pol = NodeStateInstructionRed(
+                _id=_id,
+                _start_step=_start_step,
+                _end_step=_start_step,  # only run for 1 step
+                _target_node_id=node.node_id,
+                _pol_initiator="DIRECT",
+                _pol_type=NodePOLType["SERVICE"],
+                pol_protocol=pol_service_name,
+                _pol_state=SoftwareState.COMPROMISED,
+                _pol_source_node_id=source_node.node_id,
+                _pol_source_node_service=source_node_service.name,
+                _pol_source_node_service_state=source_node_service.software_state,
+            )
+
+            self.red_node_pol[_id] = red_pol
+
+            # 2: Launch the attack from compromised node - set the IER
+
+            ier_id = str(uuid.uuid4())
+            # Launch the attack after node is compromised, and not right at the end of the episode
+            ier_start_step = randint(_start_step + 2, int(self.episode_steps * 0.8))
+            ier_end_step = self.episode_steps
+
+            # Randomise the load, as a percentage of a random link bandwith
+            ier_load = uniform(0.4, 0.8) * choice(bandwidths)
+            ier_protocol = pol_service_name  # Same protocol as compromised node
+            ier_service = node.services[pol_service_name]
+            ier_port = ier_service.port
+            ier_mission_criticality = (
+                0  # Red IER will never be important to green agent success
+            )
+            # We choose a node to attack based on the first that applies:
+            # a. Green IERs, select dest node of the red ier based on dest node of green IER
+            # b. Attack a random server that doesn't have a DENY acl rule in default config
+            # c. Attack a random server
+            possible_ier_destinations = [
+                ier.get_dest_node_id()
+                for ier in list(self.green_iers.values())
+                if ier.get_source_node_id() == node.node_id
+            ]
+            if len(possible_ier_destinations) < 1:
+                for server in servers:
+                    if not self.acl.is_blocked(
+                        node.ip_address,
+                        server.ip_address,
+                        ier_service,
+                        ier_port,
+                    ):
+                        possible_ier_destinations.append(server.node_id)
+            if len(possible_ier_destinations) < 1:
+                # If still none found choose from all servers
+                possible_ier_destinations = [server.node_id for server in servers]
+            ier_dest = choice(possible_ier_destinations)
+            self.red_iers[ier_id] = IER(
+                ier_id,
+                ier_start_step,
+                ier_end_step,
+                ier_load,
+                ier_protocol,
+                ier_port,
+                node.node_id,
+                ier_dest,
+                ier_mission_criticality,
+            )
+
+            overwhelm_pol = red_pol
+            overwhelm_pol.id = str(uuid.uuid4())
+            overwhelm_pol.end_step = self.episode_steps
+
+            # 3: Make sure the targetted node can be set to overwhelmed - with node pol
+            # # TODO remove duplicate red pol for same targetted service - must take into account start step
+
+            o_pol_id = str(uuid.uuid4())
+            o_red_pol = NodeStateInstructionRed(
+                _id=o_pol_id,
+                _start_step=ier_start_step,
+                _end_step=self.episode_steps,
+                _target_node_id=ier_dest,
+                _pol_initiator="DIRECT",
+                _pol_type=NodePOLType["SERVICE"],
+                pol_protocol=ier_protocol,
+                _pol_state=SoftwareState.OVERWHELMED,
+                _pol_source_node_id=source_node.node_id,
+                _pol_source_node_service=source_node_service.name,
+                _pol_source_node_service_state=source_node_service.software_state,
+            )
+            self.red_node_pol[o_pol_id] = o_red_pol

src/primaite/environment/reward.py

@@ -78,8 +78,8 @@ def calculate_reward_function(
         start_step = ier_value.get_start_step()
         stop_step = ier_value.get_end_step()
         if step_count >= start_step and step_count <= stop_step:
-            reference_blocked = reference_ier.get_is_running()
+            reference_blocked = not reference_ier.get_is_running()
-            live_blocked = ier_value.get_is_running()
+            live_blocked = not ier_value.get_is_running()
             ier_reward = (
                 config_values.green_ier_blocked * ier_value.get_mission_criticality()
             )

src/primaite/main.py

@@ -354,6 +354,7 @@ def run(training_config_path: Union[str, Path], lay_down_config_path: Union[str,
         transaction_list=transaction_list,
         session_path=session_dir,
         timestamp_str=timestamp_str,
+        obs_space_description=env.obs_handler.describe_structure(),
     )
 
     print("Updating Session Metadata file...")

src/primaite/nodes/node_state_instruction_red.py

@@ -1,8 +1,11 @@
 # Crown Copyright (C) Dstl 2022. DEFCON 703. Shared in confidence.
 """Defines node behaviour for Green PoL."""
+from dataclasses import dataclass
+
 from primaite.common.enums import NodePOLType
 
 
+@dataclass()
 class NodeStateInstructionRed(object):
     """The Node State Instruction class."""
 

src/primaite/nodes/service_node.py

@@ -190,14 +190,14 @@ class ServiceNode(ActiveNode):
                 service_value.reduce_patching_count()
 
     def update_resetting_status(self):
-        """Updates the resetting counter for any service that are resetting."""
+        """Update resetting counter and set software state if it reached 0."""
         super().update_resetting_status()
         if self.resetting_count <= 0:
             for service in self.services.values():
                 service.software_state = SoftwareState.GOOD
 
     def update_booting_status(self):
-        """Updates the booting counter for any service that are booting up."""
+        """Update booting counter and set software to good if it reached 0."""
         super().update_booting_status()
         if self.booting_count <= 0:
             for service in self.services.values():

src/primaite/transactions/transaction.py

@@ -20,23 +20,14 @@ class Transaction(object):
         self.episode_number = _episode_number
         self.step_number = _step_number
 
-    def set_obs_space_pre(self, _obs_space_pre):
+    def set_obs_space(self, _obs_space):
         """
         Sets the observation space (pre).
 
         Args:
             _obs_space_pre: The observation space before any actions are taken
         """
-        self.obs_space_pre = _obs_space_pre
+        self.obs_space = _obs_space
-
-    def set_obs_space_post(self, _obs_space_post):
-        """
-        Sets the observation space (post).
-
-        Args:
-            _obs_space_post: The observation space after any actions are taken
-        """
-        self.obs_space_post = _obs_space_post
 
     def set_reward(self, _reward):
         """

src/primaite/transactions/transactions_to_file.py

@@ -22,24 +22,12 @@ def turn_action_space_to_array(_action_space):
         return [str(_action_space)]
 
 
-def turn_obs_space_to_array(_obs_space, _obs_assets, _obs_features):
+def write_transaction_to_file(
-    """
+    transaction_list,
-    Turns observation space into a string array so it can be saved to csv.
+    session_path: Path,
-
+    timestamp_str: str,
-    Args:
+    obs_space_description: list,
-        _obs_space: The observation space
+):
-        _obs_assets: The number of assets (i.e. nodes or links) in the observation space
-        _obs_features: The number of features associated with the asset
-    """
-    return_array = []
-    for x in range(_obs_assets):
-        for y in range(_obs_features):
-            return_array.append(str(_obs_space[x][y]))
-
-    return return_array
-
-
-def write_transaction_to_file(transaction_list, session_path: Path, timestamp_str: str):
     """
     Writes transaction logs to file to support training evaluation.
 
@@ -56,13 +44,13 @@ def write_transaction_to_file(transaction_list, session_path: Path, timestamp_st
     # This will be tied into the PrimAITE Use Case so that they make sense
     template_transation = transaction_list[0]
     action_length = template_transation.action_space.size
-    obs_shape = template_transation.obs_space_post.shape
+    # obs_shape = template_transation.obs_space_post.shape
-    obs_assets = template_transation.obs_space_post.shape[0]
+    # obs_assets = template_transation.obs_space_post.shape[0]
-    if len(obs_shape) == 1:
+    # if len(obs_shape) == 1:
-        # bit of a workaround but I think the way transactions are written will change soon
+    # bit of a workaround but I think the way transactions are written will change soon
-        obs_features = 1
+    # obs_features = 1
-    else:
+    # else:
-        obs_features = template_transation.obs_space_post.shape[1]
+    # obs_features = template_transation.obs_space_post.shape[1]
 
     # Create the action space headers array
     action_header = []
@@ -70,16 +58,12 @@ def write_transaction_to_file(transaction_list, session_path: Path, timestamp_st
         action_header.append("AS_" + str(x))
 
     # Create the observation space headers array
-    obs_header_initial = []
+    # obs_header_initial = [f"pre_{o}" for o in obs_space_description]
-    obs_header_new = []
+    # obs_header_new = [f"post_{o}" for o in obs_space_description]
-    for x in range(obs_assets):
-        for y in range(obs_features):
-            obs_header_initial.append("OSI_" + str(x) + "_" + str(y))
-            obs_header_new.append("OSN_" + str(x) + "_" + str(y))
 
     # Open up a csv file
     header = ["Timestamp", "Episode", "Step", "Reward"]
-    header = header + action_header + obs_header_initial + obs_header_new
+    header = header + action_header + obs_space_description
 
     try:
         filename = session_path / f"all_transactions_{timestamp_str}.csv"
@@ -98,12 +82,7 @@ def write_transaction_to_file(transaction_list, session_path: Path, timestamp_st
             csv_data = (
                 csv_data
                 + turn_action_space_to_array(transaction.action_space)
-                + turn_obs_space_to_array(
+                + transaction.obs_space.tolist()
-                    transaction.obs_space_pre, obs_assets, obs_features
-                )
-                + turn_obs_space_to_array(
-                    transaction.obs_space_post, obs_assets, obs_features
-                )
             )
             csv_writer.writerow(csv_data)
 

tests/test_red_random_agent_behaviour.py

@@ -0,0 +1,77 @@
+from datetime import datetime
+
+from primaite.config.lay_down_config import data_manipulation_config_path
+from primaite.environment.primaite_env import Primaite
+from primaite.nodes.node_state_instruction_red import NodeStateInstructionRed
+from tests import TEST_CONFIG_ROOT
+from tests.conftest import _get_temp_session_path
+
+
+def run_generic(env, config_values):
+    """Run against a generic agent."""
+    # Reset the environment at the start of the episode
+    env.reset()
+    for episode in range(0, config_values.num_episodes):
+        for step in range(0, config_values.num_steps):
+            # Send the observation space to the agent to get an action
+            # TEMP - random action for now
+            # action = env.blue_agent_action(obs)
+            # action = env.action_space.sample()
+            action = 0
+
+            # Run the simulation step on the live environment
+            obs, reward, done, info = env.step(action)
+
+            # Break if done is True
+            if done:
+                break
+
+        # Reset the environment at the end of the episode
+        env.reset()
+
+    env.close()
+
+
+def test_random_red_agent_behaviour():
+    """
+    Test that hardware state is penalised at each step.
+
+    When the initial state is OFF compared to reference state which is ON.
+    """
+    list_of_node_instructions = []
+
+    # RUN TWICE so we can make sure that red agent is randomised
+    for i in range(2):
+        """Takes a config path and returns the created instance of Primaite."""
+        session_timestamp: datetime = datetime.now()
+        session_path = _get_temp_session_path(session_timestamp)
+
+        timestamp_str = session_timestamp.strftime("%Y-%m-%d_%H-%M-%S")
+        env = Primaite(
+            training_config_path=TEST_CONFIG_ROOT
+            / "one_node_states_on_off_main_config.yaml",
+            lay_down_config_path=data_manipulation_config_path(),
+            transaction_list=[],
+            session_path=session_path,
+            timestamp_str=timestamp_str,
+        )
+        # set red_agent_
+        env.training_config.random_red_agent = True
+        training_config = env.training_config
+        training_config.num_steps = env.episode_steps
+
+        run_generic(env, training_config)
+        # add red pol instructions to list
+        list_of_node_instructions.append(env.red_node_pol)
+
+    # compare instructions to make sure that red instructions are truly random
+    for index, instruction in enumerate(list_of_node_instructions):
+        for key in list_of_node_instructions[index].keys():
+            instruction: NodeStateInstructionRed = list_of_node_instructions[index][key]
+            print(f"run {index}")
+            print(f"{key} start step: {instruction.get_start_step()}")
+            print(f"{key} end step: {instruction.get_end_step()}")
+            print(f"{key} target node id: {instruction.get_target_node_id()}")
+            print("")
+
+    assert list_of_node_instructions[0].__ne__(list_of_node_instructions[1])

tests/test_reward.py

@@ -16,17 +16,26 @@ def test_rewards_are_being_penalised_at_each_step_function():
     )
 
     """
-    On different steps (of the 13 in total) these are the following rewards for config_6 which are activated:
+    The config 'one_node_states_on_off_lay_down_config.yaml' has 15 steps:
-        File System State: goodShouldBeCorrupt = 5 (between Steps 1 & 3)
+        On different steps, the laydown config has Pattern of Life (PoLs) which change a state of the node's attribute.
-        Hardware State: onShouldBeOff = -2 (between Steps 4 & 6)
+        For example, turning the nodes' file system state to CORRUPT from its original state GOOD.
-        Service State: goodShouldBeCompromised = 5 (between Steps 7 & 9)
+        As a result these are the following rewards are activated:
-        Software State (Software State): goodShouldBeCompromised = 5 (between Steps 10 & 12)
+            File System State: corrupt_should_be_good = -10 * 2 (on Steps 1 & 2)
+            Hardware State: off_should_be_on = -10 * 2 (on Steps 4 & 5)
+            Service State: compromised_should_be_good = -20 * 2 (on Steps 7 & 8)
+            Software State: compromised_should_be_good = -20 * 2 (on Steps 10 & 11)
 
-    Total Reward: -2 - 2 + 5 + 5 + 5 + 5 + 5 + 5 = 26
+            The Pattern of Life (PoLs) last for 2 steps, so the agent is penalised twice.
-    Step Count: 13
+
+    Note: This test run inherits from conftest.py where the PrimAITE environment is ran and the blue agent is hard-coded
+    to do NOTHING on every step.
+    We use Pattern of Lifes (PoLs) to change the nodes states and display that the agent is being penalised on all steps
+    where the live network node differs from the network reference node.
+
+    Total Reward: -10 + -10 + -10 + -10 + -20 + -20 + -20 + -20 = -120
+    Step Count: 15
 
     For the 4 steps where this occurs the average reward is:
-        Average Reward: 2 (26 / 13)
+        Average Reward: -8 (-120 / 15)
     """
-    print("average reward", env.average_reward)
     assert env.average_reward == -8.0