A Simulated Optimal Intrusion Response Game

Overview

Optimal Intrusion Response

An OpenAI Gym interface to a MDP/Markov Game model for optimal intrusion response of a realistic infrastructure simulated using system traces.

Included Environments

  • optimal-intrusion-response-v1
  • optimal-intrusion-response-v2
  • optimal-intrusion-response-v3

Requirements

  • Python 3.5+
  • OpenAI Gym
  • NumPy
  • jsonpickle (for configuration files)
  • torch (for baseline algorithms)

Installation

# install from pip
pip install gym-optimal-intrusion-response==1.0.0
# local install from source
$ pip install -e gym-optimal-intrusion-response
# force upgrade deps
$ pip install -e gym-optimal-intrusion-response --upgrade

# git clone and install from source
git clone https://github.com/Limmen/gym-optimal-intrusion-response
cd gym-optimal-intrusion-response
pip3 install -e .

Usage

The environment can be accessed like any other OpenAI environment with gym.make. Once the environment has been created, the API functions step(), reset(), render(), and close() can be used to train any RL algorithm of your preference.

import gym
from gym_idsgame.envs import IdsGameEnv
env_name = "optimal-intrusion-response-v1"
env = gym.make(env_name)

Infrastructure

Traces

Alert/login traces from the emulated infrastructure are available in (./traces).

Publications

@INPROCEEDINGS{hammar_stadler_cnsm_21,
AUTHOR="Kim Hammar and Rolf Stadler",
TITLE="Learning Intrusion Prevention Policies through Optimal Stopping",
BOOKTITLE="International Conference on Network and Service Management (CNSM 2021)",
ADDRESS="Izmir, Turkey",
DAYS=1,
YEAR=2021,
note={\url{http://dl.ifip.org/db/conf/cnsm/cnsm2021/1570732932.pdf}},
KEYWORDS="Network Security, automation, optimal stopping, reinforcement learning, Markov Decision Processes",
ABSTRACT="We study automated intrusion prevention using reinforcement learning. In a novel approach, we formulate the problem of intrusion prevention as an optimal stopping problem. This formulation allows us insight into the structure of the optimal policies, which turn out to be threshold based. Since the computation of the optimal defender policy using dynamic programming is not feasible for practical cases, we approximate the optimal policy through reinforcement learning in a simulation environment. To define the dynamics of the simulation, we emulate the target infrastructure and collect measurements. Our evaluations show that the learned policies are close to optimal and that they indeed can be expressed using thresholds."
}
@INPROCEEDINGS{Hamm2011:Finding,
AUTHOR="Kim Hammar and Rolf Stadler",
TITLE="Finding Effective Security Strategies through Reinforcement Learning and
{Self-Play}",
BOOKTITLE="International Conference on Network and Service Management (CNSM 2020)
(CNSM 2020)",
ADDRESS="Izmir, Turkey",
DAYS=1,
MONTH=nov,
YEAR=2020,
KEYWORDS="Network Security; Reinforcement Learning; Markov Security Games",
ABSTRACT="We present a method to automatically find security strategies for the use
case of intrusion prevention. Following this method, we model the
interaction between an attacker and a defender as a Markov game and let
attack and defense strategies evolve through reinforcement learning and
self-play without human intervention. Using a simple infrastructure
configuration, we demonstrate that effective security strategies can emerge
from self-play. This shows that self-play, which has been applied in other
domains with great success, can be effective in the context of network
security. Inspection of the converged policies show that the emerged
policies reflect common-sense knowledge and are similar to strategies of
humans. Moreover, we address known challenges of reinforcement learning in
this domain and present an approach that uses function approximation, an
opponent pool, and an autoregressive policy representation. Through
evaluations we show that our method is superior to two baseline methods but
that policy convergence in self-play remains a challenge."
}
@misc{hammar2021intrusion,
      title={Intrusion Prevention through Optimal Stopping}, 
      author={Kim Hammar and Rolf Stadler},
      year={2021},
      eprint={2111.00289},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

See also

Author & Maintainer

Kim Hammar [email protected]

Copyright and license

LICENSE

Creative Commons

(C) 2021, Kim Hammar

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Comments
  • gym-optimal-intrusion-response cannot gym.make

    gym-optimal-intrusion-response cannot gym.make

    After I installed gym-optimal-intrusion-response

    # git clone and install from source git clone https://github.com/Limmen/gym-optimal-intrusion-response cd gym-optimal-intrusion-response pip3 install -e .

    I use it by

    import gym from gym_idsgame.envs import IdsGameEnv env_name = "optimal-intrusion-response-v1" env = gym.make(env_name)

    but I had a problem

    gym.error.UnregisteredEnv: No registered env with id: optimal-intrusion-response-v1

    opened by wangzepeng111 4
  • May I ask you for how to start this project?

    May I ask you for how to start this project?

    I had read your paper Learning Intrusion Prevention Policies through Optimal Stopping, and have some problems,such as the defender policy against NOISYATTACKER and STEALTHYATTACKER, I don't know how it works. And your code import gym_pycr_ctf but I can't find this function

    opened by Arashiailing 2
Releases(1.0.0)
Owner
Kim Hammar
PhD @KTH, ML, Distributed systems, security & stuff. Previously @logicalclocks, Allstate, Ericsson.
Kim Hammar
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