Optimizing Cyber Response Time on Temporal Active Directory Networks Using Decoys

Core Concepts

Maximizing defender's response time in Active Directory networks using decoys.

Abstract

• Abstract:
  • Study on placing decoys in Active Directory networks.
  • Introduction to temporal attack graphs and response time metric.
• Introduction:
  • Active Directory as a target for cyber adversaries.
  • Dynamic nature of AD graphs affecting security.
• Model Description:
  • Definition of temporal directed attack graph.
  • Formulation of the problem as a Stackelberg game.
• Related Work:
  • Previous research on identity snowball attacks and defense strategies.
• Proposed Methodology:
  • Algorithm for computing optimal attack paths.
  • Faster computation for earliest-arrival paths using Dijkstra.
• EDO Algorithm for max-RT:
  • Application of Evolutionary Diversity Optimization.
  • Challenges with infeasible solutions and proposed repair operators.
• Surrogate-assisted and penalty-based repair operator:
  • Algorithm for evaluating populations using a surrogate fitness function.

Stats

Response time is defined as the duration from the moment attackers trigger the first decoy to when they compromise the DA.
The algorithm for computing optimal attack paths is based on finding the earliest-arrival path.
The Dijkstra-based algorithm for computing earliest-arrival paths is more efficient than Wu's algorithm.
The EDO algorithm aims to acquire diverse defensive plans for maximizing response time.

Quotes

Key Insights Distilled From

Optimizing Cyber Response Time on Temporal Active Directory Networks Using Decoys

by Huy Q. Ngo,M... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18162.pdf

Deeper Inquiries

How can the proposed repair operators handle the issue of infeasible solutions effectively

The proposed repair operators can effectively handle the issue of infeasible solutions by providing mechanisms to transform these solutions into feasible ones. The Integer Programming (IP) repair operator works by probabilistically unblocking nodes that were previously blocked in the infeasible solution. This creates space for the repair process to take place. The IP repair operator then formulates and solves an Integer Programming problem to adjust the solution and ensure it meets the constraints of the problem. On the other hand, the surrogate-assisted and penalty-based repair operator utilizes a lightweight surrogate fitness function to evaluate the population in each iteration. This allows for a more efficient evaluation process, focusing on a set of "important" paths rather than the entire graph. By using these repair operators, the algorithm can guide the population towards feasible solutions by iteratively adjusting and improving the solutions.

What are the implications of using a surrogate fitness function in the EDO algorithm

The implications of using a surrogate fitness function in the EDO algorithm are significant. By employing a surrogate fitness function, the algorithm can evaluate the population more efficiently, especially in scenarios where the complete fitness function is computationally expensive. The surrogate function focuses on evaluating a set of "important" paths, which are likely to have a significant impact on the overall solution quality. This approach reduces the computational burden of evaluating the entire graph in each iteration, leading to faster convergence and improved scalability of the algorithm. Additionally, the surrogate fitness function allows for a more targeted evaluation, ensuring that the algorithm focuses on critical aspects of the problem space, ultimately enhancing the algorithm's performance and effectiveness.

How can the findings of this study be applied to real-world cybersecurity scenarios beyond Active Directory networks

The findings of this study can be applied to real-world cybersecurity scenarios beyond Active Directory networks by providing insights into effective defense strategies against cyber threats. The optimization of cyber response time using decoys in temporal attack graphs can be translated into practical cybersecurity measures for organizations. By strategically placing decoys or honeypots in network environments, security teams can detect and respond to potential attacks more effectively, reducing the impact of security breaches. The use of Evolutionary Diversity Optimization (EDO) algorithms and repair operators can enhance the resilience of cybersecurity defenses, ensuring that organizations can proactively defend against evolving cyber threats. Implementing these strategies can strengthen overall cybersecurity posture and improve incident response capabilities in real-world cybersecurity scenarios.