An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents

• URL: http://arxiv.org/abs/2501.19206v1
• Date: Fri, 31 Jan 2025 15:15:02 GMT
• Title: An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents
• Authors: Gregory Palmer, Luke Swaby, Daniel J. B. Harrold, Matthew Stewart, Alex Hiles, Chris Willis, Ian Miles, Sara Farmer,
• Abstract summary: We introduce and evaluate a theoretically-sound, potential-based reward shaping approach to expedite this process. In addition, given the increasing number of open-source ACD-DRL approaches, we extend the DO formulation to allow for multiple response oracles.
• Score: 0.0
• License:
• Abstract: The recent rise in increasingly sophisticated cyber-attacks raises the need for robust and resilient autonomous cyber-defence (ACD) agents. Given the variety of cyber-attack tactics, techniques and procedures (TTPs) employed, learning approaches that can return generalisable policies are desirable. Meanwhile, the assurance of ACD agents remains an open challenge. We address both challenges via an empirical game-theoretic analysis of deep reinforcement learning (DRL) approaches for ACD using the principled double oracle (DO) algorithm. This algorithm relies on adversaries iteratively learning (approximate) best responses against each others' policies; a computationally expensive endeavour for autonomous cyber operations agents. In this work we introduce and evaluate a theoretically-sound, potential-based reward shaping approach to expedite this process. In addition, given the increasing number of open-source ACD-DRL approaches, we extend the DO formulation to allow for multiple response oracles (MRO), providing a framework for a holistic evaluation of ACD approaches.

Related papers

• A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges [2.2448567386846916]
  Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI) This paper presents a comprehensive survey of RL, meticulously analyzing a wide range of algorithms. We offer practical insights into the selection and implementation of RL algorithms, addressing common challenges like convergence, stability, and the exploration-exploitation dilemma.
  arXiv  Detail & Related papers  (2024-11-28T03:53:14Z)
• Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence [0.0]
  Cyber-attacks pose a security threat to military command and control networks, Intelligence, Surveillance, and Reconnaissance (ISR) systems, and civilian critical national infrastructure. The use of artificial intelligence and autonomous agents in these attacks increases the scale, range, and complexity of this threat and the subsequent disruption they cause. Autonomous Cyber Defence (ACD) agents aim to mitigate this threat by responding at machine speed and at the scale required to address the problem.
  arXiv  Detail & Related papers  (2024-11-26T16:51:52Z)
• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process. We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks [51.00472376469131]
  We propose a framework that learns a robust caregiver's policy by training it for diverse care-receiver responses. We demonstrate that policies trained with a popular deep RL method are vulnerable to changes in policies of other agents.
  arXiv  Detail & Related papers  (2024-03-01T08:15:18Z)
• On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics [0.7655800373514546]
  Risk-aware Reinforcement Learning algorithms were shown to outperform risk-neutral counterparts in a variety of continuous-action tasks. The theoretical basis for the pessimistic objectives these algorithms employ remains unestablished. We propose Dual Actor-Critic (DAC) as a risk-aware, model-free algorithm that features two distinct actor networks.
  arXiv  Detail & Related papers  (2023-10-30T13:28:06Z)
• Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey [0.0]
  The rapid increase in the number of cyber-attacks in recent years raises the need for principled methods for defending networks against malicious actors. Deep reinforcement learning has emerged as a promising approach for mitigating these attacks. While DRL has shown much potential for cyber defence, numerous challenges must be overcome before DRL can be applied to the autonomous cyber defence problem at scale.
  arXiv  Detail & Related papers  (2023-10-11T16:24:14Z)
• On Autonomous Agents in a Cyber Defence Environment [0.0]
  We explore the utility of the autonomous cyber operation environments presented as part of the Cyber Autonomy Gym for Experimentation. CAGE Challenge 2 required a defensive Blue agent to defend a network from an attacking Red agent. We identify four classes of algorithms, namely, Single- Agent Deep Reinforcement Learning (DRL), Hierarchical DRL, Ensembles, and Non-DRL approaches.
  arXiv  Detail & Related papers  (2023-09-14T02:09:36Z)
• Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach [56.84948632954274]
  This paper investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network. We propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network.
  arXiv  Detail & Related papers  (2021-12-20T15:45:28Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Robust Deep Reinforcement Learning through Adversarial Loss [74.20501663956604]
  Recent studies have shown that deep reinforcement learning agents are vulnerable to small adversarial perturbations on the agent's inputs. We propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2020-08-05T07:49:42Z)
• Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations [88.94162416324505]
  A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises. Since the observations deviate from the true states, they can mislead the agent into making suboptimal actions. We show that naively applying existing techniques on improving robustness for classification tasks, like adversarial training, is ineffective for many RL tasks.
  arXiv  Detail & Related papers  (2020-03-19T17:59:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.