Improving Cyber Security in Operational Technology Systems using Reinforcement Learning with Action Masking and Curriculum Learning

Keskeiset käsitteet

Applying action masking and curriculum learning techniques can significantly improve the data efficiency and overall performance of reinforcement learning agents in remediating cyber attacks on operational technology systems.

Tiivistelmä

This paper explores the use of reinforcement learning (RL) to train defensive agents in a simulated Integrated Platform Management System (IPMS) environment under cyber attack, known as IPMSRL. The authors introduce three environment configurations of varying difficulty, with the hard configuration incorporating realistic dynamics such as false positive alerts, false negative alerts, and alert delays.

The authors first establish a baseline using a standard PPO RL agent, which struggles to perform well in the more difficult environment configurations. To address this, they explore two guided RL techniques:

1. Curriculum Learning (CL): The authors gradually increase the difficulty of the environment during training, allowing the agent to first learn in simpler scenarios before transitioning to more complex ones. This approach is shown to significantly improve the agent's performance, reaching a mean episode reward of -0.569 in the hard environment, compared to -2.791 for the baseline.

2. Action Masking (AM): The authors implement action masking to constrain the agent's available actions based on the current state of the environment, preventing it from taking undesirable or impossible actions. This technique also leads to substantial improvements, with the agent reaching a mean episode reward of -0.743 in the hard environment.

Finally, the authors combine CL and AM, which results in the highest level of performance observed, with a mean episode reward of 0.137 in the hard environment. This outperforms both the baseline and a hardcoded defensive agent, which achieved a mean episode reward of -1.895 in the hard environment.

The results demonstrate that the application of CL and AM, individually and in combination, can significantly enhance the data efficiency and overall performance of RL agents in the context of operational technology cyber security, where complex real-world dynamics need to be addressed.

Tilastot

The paper provides the following key metrics:

Baseline PPO agent's mean episode reward in the easy, medium, and hard environment configurations: 0.977, 0.104, and -2.791 respectively.
Hardcoded defensive agent's mean episode reward in the easy, medium, and hard environment configurations: 0.988, 0.883, and -1.895 respectively.
Curriculum learning agent's mean episode reward in the hard environment configuration: -0.569.
Action masking agent's mean episode reward in the hard environment configuration: -0.743.
Combined curriculum learning and action masking agent's mean episode reward in the hard environment configuration: 0.137.

Lainaukset

"Applying curriculum learning, in the most difficult environment tested, resulted in an episode reward mean increasing from a baseline result of -2.791 to -0.569."
"Applying action masking, in the most difficult environment tested, resulted in an episode reward mean increasing from a baseline result of -2.791 to -0.743."
"The training method which resulted in the highest level of performance observed in this paper was a combination of the application of curriculum learning and action masking, with a mean episode reward of 0.137."

Tärkeimmät oivallukset

Applying Action Masking and Curriculum Learning Techniques to Improve Data Efficiency and Overall Performance in Operational Technology Cyber Security using Reinforcement Learning

by Alec Wilson,... klo arxiv.org 09-18-2024

https://arxiv.org/pdf/2409.10563.pdf

Syvällisempiä Kysymyksiä

How could the curriculum learning approach be further optimized to achieve even higher performance, such as by dynamically adjusting the task difficulty based on the agent's learning progress?

To optimize the curriculum learning (CL) approach for even higher performance, a dynamic adjustment mechanism could be implemented that tailors task difficulty based on real-time assessments of the agent's learning progress. This could involve the following strategies:

Performance Metrics Monitoring: Continuously monitor key performance indicators (KPIs) such as mean episode reward, win rate, or convergence speed. By establishing thresholds for these metrics, the curriculum can adaptively increase or decrease the difficulty of tasks. For instance, if the agent consistently achieves a high reward in a given difficulty level, the curriculum could automatically escalate to the next level of complexity.

Adaptive Task Switching: Instead of fixed timesteps for task transitions, employ a reinforcement learning-based approach to determine when to switch tasks. This could involve using a meta-learning framework where a secondary agent learns the optimal timing for task transitions based on the primary agent's performance.

Feedback Loops: Implement feedback mechanisms that allow the agent to signal when it feels ready for increased difficulty. This could be based on internal confidence levels or the stability of its learned policy, allowing for a more personalized learning experience.

Multi-Stage Curriculum: Introduce a multi-stage curriculum that not only varies the difficulty but also the types of challenges presented. For example, after mastering basic tasks, the agent could be exposed to scenarios that require it to handle multiple simultaneous threats, thereby enhancing its adaptability and robustness.

Transfer Learning: Leverage transfer learning techniques to allow the agent to retain knowledge from previous tasks and apply it to new, more complex scenarios. This could involve fine-tuning the agent's policy on simpler tasks before introducing it to more challenging environments.

By implementing these strategies, the curriculum learning approach can become more responsive to the agent's capabilities, ultimately leading to improved data efficiency and performance in operational technology cyber security.

What other types of constraints or safety-critical criteria could be incorporated into the action masking approach to better align the agent's behavior with real-world operational technology security requirements?

Incorporating additional constraints and safety-critical criteria into the action masking approach can significantly enhance the alignment of the agent's behavior with real-world operational technology (OT) security requirements. Some potential enhancements include:

Risk Assessment Criteria: Introduce risk assessment metrics that evaluate the potential impact of each action on the system's overall security posture. Actions that could lead to high-risk scenarios, such as those that expose critical nodes to further vulnerabilities, could be masked.

Resource Availability Constraints: Implement constraints based on the availability of system resources (e.g., bandwidth, processing power). For instance, if a node is under heavy load, actions that require significant resources could be masked to prevent system overload.

Temporal Constraints: Incorporate timing-based constraints that consider the urgency of actions. For example, if a critical node is compromised, the agent should prioritize containment actions within a specific timeframe, masking other less urgent actions until the immediate threat is addressed.

Compliance and Regulatory Requirements: Integrate compliance checks that ensure the agent's actions adhere to industry regulations and best practices. Actions that violate these standards could be masked, ensuring that the agent operates within legal and ethical boundaries.

Human-in-the-Loop Mechanisms: Allow for human oversight in critical decision-making scenarios. The action masking could be adjusted to require human approval for certain high-stakes actions, ensuring that the agent's behavior aligns with organizational policies and human judgment.

Historical Incident Data: Utilize historical data on past incidents to inform action masking. Actions that have previously led to security breaches or failures could be masked in similar future scenarios, allowing the agent to learn from past mistakes.

By integrating these constraints into the action masking framework, the agent can operate more safely and effectively within the complex landscape of operational technology security, ultimately enhancing its performance and reliability.

Given the potential benefits of the combined curriculum learning and action masking approach, how could these techniques be applied to other complex, safety-critical domains beyond operational technology cyber security?

The combined techniques of curriculum learning (CL) and action masking (AM) can be effectively applied to various complex, safety-critical domains beyond operational technology cyber security. Here are several potential applications:

Autonomous Vehicles: In the realm of autonomous driving, CL can be used to progressively introduce the vehicle to increasingly complex driving scenarios, such as urban environments, highway driving, and adverse weather conditions. AM can be employed to mask actions that could lead to unsafe maneuvers, such as speeding or abrupt lane changes, ensuring that the vehicle adheres to safety protocols.

Healthcare Robotics: In healthcare settings, robots can be trained using CL to perform tasks ranging from simple patient interactions to complex surgical procedures. AM can help ensure that the robot only performs actions that are safe and appropriate for the patient's condition, masking actions that could lead to harm or violate medical protocols.

Aerospace and Aviation: In aviation, CL can facilitate the training of pilots and autonomous drones by gradually increasing the complexity of flight scenarios, such as emergency landings or navigation through congested airspace. AM can be used to mask actions that could compromise flight safety, such as exceeding altitude limits or entering restricted airspace.

Industrial Automation: In manufacturing and industrial settings, CL can be applied to train robots for assembly tasks, starting with simple components and progressing to complex assemblies. AM can ensure that robots do not perform actions that could lead to equipment damage or safety hazards, such as operating machinery without proper safety checks.

Cyber-Physical Systems: In smart grid management or critical infrastructure, CL can help agents learn to manage energy distribution or respond to system failures. AM can mask actions that could lead to system overloads or failures, ensuring that the agent operates within safe operational limits.

Disaster Response: In emergency response scenarios, CL can be used to train agents to handle various disaster situations, from minor incidents to large-scale emergencies. AM can help mask actions that could exacerbate the situation, such as deploying resources in a way that could lead to further chaos or danger.

By leveraging the strengths of CL and AM in these domains, organizations can enhance the safety, efficiency, and effectiveness of their systems, ultimately leading to better outcomes in high-stakes environments.