README.md

Experiment tabular_q_agentvsdefend_minimal

This is an experiment in the idsgame-v4 environment. An environment where neither the attacker nor defender is part of the environment, i.e. it is intended for 2-agent simulations or RL training.

In this experiment, the attacker is implemented with a greedy policy based on a save Q-table. The defender is implemented with the policy defend_minimal. The defend_minimal policy entails that the defender will always defend the attribute with the minimal value out of all of its neighbors.

The network configuration of the environment is as follows:

• num_layers=4 (number of layers between the start and end nodes)
• num_servers_per_layer=5
• num_attack_types=10
• max_value=9

The starting state for each node in the environment is initialized as follows (with some randomness for where the vulnerabilities are placed).

• defense_val=2
• attack_val=0
• num_vulnerabilities_per_node=1 (which type of defense at the node that is vulnerable is selected randomly when the environment is initialized)
• det_val=2
• vulnerability_val=0
• num_vulnerabilities_per_layer=5

Environment

• Env: idsgame-v4

Algorithm

• Bots

Instructions

To configure the experiment use the config.json file. Alternatively, it is also possible to delete the config file and edit the configuration directly in run.py (this will cause the configuration to be overridden on the next run).

Example configuration in config.json:

{
    "attacker_type": 0,
    "defender_type": 2,
    "env_name": "idsgame-v4",
    "idsgame_config": null,
    "initial_state_path": "/home/kim/storage/workspace/gym-idsgame/experiments/simulations/v4/tabular_q_agent_vs_defend_minimal/initial_state/initial_state.pkl",
    "logger": null,
    "mode": 2,
    "output_dir": "/home/kim/storage/workspace/gym-idsgame/experiments/simulations/v4/tabular_q_agent_vs_defend_minimal",
    "py/object": "gym_idsgame.config.client_config.ClientConfig",
    "q_agent_config": {
        "alpha": 0.1,
        "attacker": true,
        "defender": false,
        "epsilon": 0.9,
        "epsilon_decay": 0.999,
        "eval_episodes": 1,
        "eval_frequency": 1000,
        "eval_log_frequency": 1,
        "eval_render": false,
        "eval_sleep": 0.35,
        "gamma": 0.8,
        "gif_dir": null,
        "gifs": false,
        "load_path": "/home/kim/storage/workspace/gym-idsgame/experiments/simulations/v4/tabular_q_agent_vs_defend_minimal/q_table/q_table.npy",
        "logger": null,
        "min_epsilon": 0.1,
        "num_episodes": 5000,
        "py/object": "gym_idsgame.agents.tabular_q_learning.q_agent_config.QAgentConfig",
        "render": false,
        "save_dir": null,
        "train_log_frequency": 100,
        "video": false,
        "video_dir": null,
        "video_fps": 5,
        "video_frequency": 1
    },
    "simulation_config": {
        "gif_dir": "/home/kim/storage/workspace/gym-idsgame/experiments/simulations/v4/tabular_q_agent_vs_defend_minimal/gifs",
        "gifs": true,
        "log_frequency": 1,
        "logger": null,
        "num_episodes": 1000,
        "py/object": "gym_idsgame.simulation.dao.simulation_config.SimulationConfig",
        "render": true,
        "sleep": 0.8,
        "video": true,
        "video_dir": "/home/kim/storage/workspace/gym-idsgame/experiments/simulations/v4/tabular_q_agent_vs_defend_minimal/videos",
        "video_fps": 5,
        "video_frequency": 1
    },
    "title": "TabularQAgentAttacker vs DefendMinimalDefender"
}

Example configuration in run.py:

simulation_config = SimulationConfig(render=True, sleep=0.8, video=True, log_frequency=1,
                                     video_fps=5, video_dir=default_output_dir() + "/videos", num_episodes=1000,
                                     gifs=True, gif_dir=default_output_dir() + "/gifs", video_frequency = 1)
q_agent_config = QAgentConfig(load_path=default_output_dir() + "/q_table/q_table.npy")
env_name = "idsgame-v4"
client_config = ClientConfig(env_name=env_name, attacker_type=AgentType.TABULAR_Q_AGENT.value,
                             defender_type=AgentType.DEFEND_MINIMAL_VALUE.value, mode=RunnerMode.SIMULATE.value,
                             simulation_config=simulation_config, output_dir=default_output_dir(),
                             title="TabularQAgentAttacker vs DefendMinimalDefender",
                             q_agent_config=q_agent_config,
                             initial_state_path = default_output_dir() + "/initial_state/initial_state.pkl")

Example Simulation

Commands

Below is a list of commands for running the experiment

Run

Option 1:

./run.sh

Option 2:

make all

Option 3:

python run.py

Run Server (Without Display)

Option 1:

./run_server.sh

Option 2:

make run_server

Clean

make clean