#2459 back-sync b8 changes into core

src/primaite/game/agent/scripted_agents/random_agent.py

@@ -1,8 +1,13 @@
-from typing import Dict, Tuple
+import random
+from typing import Dict, Optional, Tuple
 
 from gymnasium.core import ObsType
+from pydantic import BaseModel
 
+from primaite.game.agent.actions import ActionManager
 from primaite.game.agent.interface import AbstractScriptedAgent
+from primaite.game.agent.observations.observation_manager import ObservationManager
+from primaite.game.agent.rewards import RewardFunction
 
 
 class RandomAgent(AbstractScriptedAgent):
@@ -19,3 +24,60 @@ class RandomAgent(AbstractScriptedAgent):
         :rtype: Tuple[str, Dict]
         """
         return self.action_manager.get_action(self.action_manager.space.sample())
+
+
+class PeriodicAgent(AbstractScriptedAgent):
+    """Agent that does nothing most of the time, but executes application at regular intervals (with variance)."""
+
+    class Settings(BaseModel):
+        """Configuration values for when an agent starts performing actions."""
+
+        start_step: int = 20
+        "The timestep at which an agent begins performing it's actions."
+        start_variance: int = 5
+        "Deviation around the start step."
+        frequency: int = 5
+        "The number of timesteps to wait between performing actions."
+        variance: int = 0
+        "The amount the frequency can randomly change to."
+        max_executions: int = 999999
+        "Maximum number of times the agent can execute its action."
+
+    def __init__(
+        self,
+        agent_name: str,
+        action_space: ActionManager,
+        observation_space: ObservationManager,
+        reward_function: RewardFunction,
+        settings: Optional[Settings] = None,
+    ) -> None:
+        """Initialise PeriodicAgent."""
+        super().__init__(
+            agent_name=agent_name,
+            action_space=action_space,
+            observation_space=observation_space,
+            reward_function=reward_function,
+        )
+        self.settings = settings or PeriodicAgent.Settings()
+        self._set_next_execution_timestep(timestep=self.settings.start_step, variance=self.settings.start_variance)
+        self.num_executions = 0
+
+    def _set_next_execution_timestep(self, timestep: int, variance: int) -> None:
+        """Set the next execution timestep with a configured random variance.
+
+        :param timestep: The timestep when the next execute action should be taken.
+        :type timestep: int
+        :param variance: Uniform random variance applied to the timestep
+        :type variance: int
+        """
+        random_increment = random.randint(-variance, variance)
+        self.next_execution_timestep = timestep + random_increment
+
+    def get_action(self, obs: ObsType, timestep: int) -> Tuple[str, Dict]:
+        """Do nothing, unless the current timestep is the next execution timestep, in which case do the action."""
+        if timestep == self.next_execution_timestep and self.num_executions < self.settings.max_executions:
+            self.num_executions += 1
+            self._set_next_execution_timestep(timestep + self.settings.frequency, self.settings.variance)
+            return "NODE_APPLICATION_EXECUTE", {"node_id": 0, "application_id": 0}
+
+        return "DONOTHING", {}

src/primaite/game/agent/scripted_agents/tap001.py

@@ -0,0 +1,78 @@
+import random
+from typing import Dict, Tuple
+
+from gymnasium.core import ObsType
+
+from primaite.game.agent.interface import AbstractScriptedAgent
+
+
+class TAP001(AbstractScriptedAgent):
+    """
+    TAP001 | Mobile Malware -- Ransomware Variant.
+
+    Scripted Red Agent. Capable of one action; launching the kill-chain (Ransomware Application)
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.setup_agent()
+
+    next_execution_timestep: int = 0
+    starting_node_idx: int = 0
+    installed: bool = False
+
+    def _set_next_execution_timestep(self, timestep: int) -> None:
+        """Set the next execution timestep with a configured random variance.
+
+        :param timestep: The timestep to add variance to.
+        """
+        random_timestep_increment = random.randint(
+            -self.agent_settings.start_settings.variance, self.agent_settings.start_settings.variance
+        )
+        self.next_execution_timestep = timestep + random_timestep_increment
+
+    def get_action(self, obs: ObsType, timestep: int) -> Tuple[str, Dict]:
+        """Waits until a specific timestep, then attempts to execute the ransomware application.
+
+        This application acts a wrapper around the kill-chain, similar to green-analyst and
+        the previous UC2 data manipulation bot.
+
+        :param obs: Current observation for this agent.
+        :type obs: ObsType
+        :param timestep: The current simulation timestep, used for scheduling actions
+        :type timestep: int
+        :return: Action formatted in CAOS format
+        :rtype: Tuple[str, Dict]
+        """
+        if timestep < self.next_execution_timestep:
+            return "DONOTHING", {}
+
+        self._set_next_execution_timestep(timestep + self.agent_settings.start_settings.frequency)
+
+        if not self.installed:
+            self.installed = True
+            return "NODE_APPLICATION_INSTALL", {
+                "node_id": self.starting_node_idx,
+                "application_name": "RansomwareScript",
+                "ip_address": self.ip_address,
+            }
+
+        return "NODE_APPLICATION_EXECUTE", {"node_id": self.starting_node_idx, "application_id": 0}
+
+    def setup_agent(self) -> None:
+        """Set the next execution timestep when the episode resets."""
+        self._select_start_node()
+        self._set_next_execution_timestep(self.agent_settings.start_settings.start_step)
+        for n, act in self.action_manager.action_map.items():
+            if not act[0] == "NODE_APPLICATION_INSTALL":
+                continue
+            if act[1]["node_id"] == self.starting_node_idx:
+                self.ip_address = act[1]["ip_address"]
+                return
+        raise RuntimeError("TAP001 agent could not find database server ip address in action map")
+
+    def _select_start_node(self) -> None:
+        """Set the starting starting node of the agent to be a random node from this agent's action manager."""
+        # we are assuming that every node in the node manager has a data manipulation application at idx 0
+        num_nodes = len(self.action_manager.node_names)
+        self.starting_node_idx = random.randint(0, num_nodes - 1)