Deep reinforcement learning for red teaming
A brief look at what DLR is and how it can be used to develop autonomous agents capable of penetration testing.
Creating an autonomous hacking agent that uses Deep Reinforcement Learning (DRL) to exploit vulnerabilities is an intriguing idea that blends cyber with advanced AI techniques. Let’s walk through how DRL could be applied to create such an agent, and break it down into stages.
(Deep) Reinforcement Learning
First, let's briefly cover the basics of RL to help us understand DRL.
What is reinforcement learning (RL)?
Reinforcement Learning is a type of machine learning where an agent learns how to behave in an environment by performing actions and receiving rewards. The goal of the agent is to maximise the cumulative reward over time.
Components of RL:
Agent: The learner or decision maker (e.g., a robot, algorithm, or AI).
Environment: The external system the agent interacts with (e.g., a game, physical world, or simulated environment).
State (S): A representation of the current situation or context the agent is in.
Action (A): A set of possible moves or decisions the agent can make in each state.
Reward (R): Feedback from the environment based on the agent’s action. It can be positive (reward) or negative (punishment).
Policy (π): The strategy that the agent follows to decide which actions to take. It maps states to actions.
Value Function (V): Measures how good it is for the agent to be in a specific state. It estimates the future rewards from that state.
Q-Value (Q-function): Measures the value of taking a specific action in a specific state.
The RL process:
The agent observes the current state of the environment.
The agent chooses an action based on its policy.
The agent receives a reward from the environment and observes the new state.
The agent updates its policy based on this new experience and tries to improve its future decisions.
What is Deep Reinforcement Learning (DRL)?
Deep Reinforcement Learning is an extension of RL that leverages Deep Learning to solve more complex problems.
In traditional RL, if the state or action spaces are large or continuous (for example, in video games or robotics), it becomes difficult to handle them using simple RL methods. Deep Reinforcement Learning uses Deep Neural Networks to approximate the policy, value, or Q-function, making it possible for the agent to handle complex, high-dimensional inputs.
How DRL Works:
Instead of manually crafting a policy or value function, a neural network is trained to approximate them.
The neural network takes the state of the environment as input and outputs actions (or estimates of the Q-values).
The network is trained using gradient-based optimisation techniques, with rewards guiding the training process.
Deep reinforcement learning with adversarial agents
Now the fun stuff.
1. Define the environment (the target system):
The target system or network that the agent interacts with will serve as the environment in which the agent operates. The environment can include a variety of components:
Servers, applications, or devices: Represent different states and entry points.
Vulnerabilities: The weaknesses in the system that can be exploited.
Security mechanisms: Firewalls, Intrusion Detection Systems (IDS), or encryption layers that the agent needs to bypass or exploit.
In this case, the agent will receive feedback (rewards) based on its success in navigating and compromising the system.
State representation:
The state could be a detailed snapshot of the system, including information about open ports, services, software versions, credentials, and other indicators of security posture.
A high-dimensional representation of the system's state could be used (e.g., network graphs, memory states), and DRL models can use these representations as input.
2. Define the actions (exploit attempts):
The actions the hacking agent can take would represent different types of exploits or techniques it can attempt to compromise the system:
Reconnaissance actions: Port scanning, network mapping, service enumeration.
Exploitation actions: Buffer overflow attempts, SQL injection, privilege escalation.
Lateral movement: Once compromised, the agent may try to move to different systems within the network.
Evasion actions: Techniques for bypassing detection systems, like evading firewalls or anti-virus software.
The agent’s action space would consist of all the possible attacks and methods at its disposal, as well as different system interactions, allowing it to choose which vulnerabilities to exploit.
3. Define rewards (success or failure of exploits):
Reinforcement learning agents need feedback, which comes in the form of rewards. For a hacking agent, rewards could be structured around different types of successes or failures:
Positive rewards:
Successfully exploiting a vulnerability.
Gaining unauthorised access to a system or resource.
Extracting sensitive data or achieving lateral movement within the network.
Disabling or bypassing a security system.
Negative rewards:
Triggering security alarms (IDS/IPS) or getting blocked by firewalls.
Crashing the system (accidentally triggering denial of service without intent).
Failing to exploit a vulnerability.
Detection by security monitoring systems.
The reward system should incentivise the agent to remain stealthy, avoid detection, and successfully exploit the most critical vulnerabilities.
4. Use DRL to train the agent:
Since the environment (the system or network) is complex and dynamic, DRL would be an ideal approach because of its ability to handle high-dimensional input (like network graphs) and learn strategies through trial and error.
Here’s how DRL fits into this:
State encoding using neural networks: Given the complexity of the target system’s state, DRL would use a neural network to process the large input space (such as network topologies or service configurations) into a usable state representation.
Q-learning or Policy Gradient algorithms: Use DRL algorithms like Deep Q-Networks (DQN) or Proximal Policy Optimisation (PPO) to learn the optimal actions to exploit vulnerabilities in the system.
The agent will train by simulating various attack scenarios on a simulated or real network, adjusting its policy to maximise rewards over time.
In complex multi-step scenarios, where the agent must chain multiple exploits together (such as gaining initial access, escalating privileges, and moving laterally), algorithms like A3C (Asynchronous Advantage Actor-Critic) could be useful.
5. Incorporate advanced hacking techniques into the DRL framework:
DRL can be used not only for simple exploits but also for more advanced techniques:
Adversarial tactics: The agent can learn to inject malicious payloads that adapt to the system’s defences in real-time.
Zero-day exploits: The agent could potentially explore novel paths or sequences of actions that lead to discovering previously unknown vulnerabilities.
Evasion techniques: DRL can be used to teach the agent how to avoid detection from Intrusion Detection Systems (IDS) or firewalls by learning which actions are less likely to trigger alerts.
6. Continuous Learning and Adaptation:
Once deployed, the agent could be designed to continuously learn and adapt to new environments. Using a combination of transfer learning and continual learning, the agent could generalise across different networks, becoming more effective over time.
Challenges and considerations:
Complexity of real-world systems: Real systems can be highly complex and dynamic, with constantly changing configurations, making it hard for an agent to adapt quickly.
Reward shaping: Defining the reward function carefully is key. If rewards are too simplistic (e.g., just based on exploitation success), the agent may take shortcuts that don't mirror real-world attack scenarios.
Adversarial responses: Security systems may evolve to counter the agent’s behaviour, so continual retraining and adaptive strategies will be necessary.
RL Shows Promise, But Scalability Needs Improvement
Similar work
Autonomous Penetration Testing using Reinforcement Learning - Jonathon Schwartz, University of Queensland.
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