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Optimally Blending Honeypots into Production Networks: Hardness and Algorithms

Md Mahabub Uz Zaman, Liangde Tao, Mark Maldonado, Chang Liu, Ahmed Sunny, Shouhuai Xu, Lin Chen

TL;DR

The paper addresses optimal deployment of honeypots blended into production networks to maximize exposure of attackers to new attacks while limiting defender losses. It formalizes the Honeypot Deployment (HD) problem as a combinatorial optimization under budget and attacker reconnaissance uncertainty, and proves the decision version is NP-hard. The authors develop an exact dynamic-programming method and a Polynomial-Time Approximation Scheme (PTAS) to compute near-optimal honeypot placements, with runtime scaling polynomially in key parameters. They also conduct simulations to explore how attacker capabilities and risk attitudes affect effectiveness, showing the approach can significantly reduce expected losses under certain reconnaissance levels. The work provides a rigorous foundation for blending honeypots and offers practical algorithms for deployment in enterprise networks.

Abstract

Honeypot is an important cyber defense technique that can expose attackers new attacks. However, the effectiveness of honeypots has not been systematically investigated, beyond the rule of thumb that their effectiveness depends on how they are deployed. In this paper, we initiate a systematic study on characterizing the cybersecurity effectiveness of a new paradigm of deploying honeypots: blending honeypot computers (or IP addresses) into production computers. This leads to the following Honeypot Deployment (HD) problem, How should the defender blend honeypot computers into production computers to maximize the utility in forcing attackers to expose their new attacks while minimizing the loss to the defender in terms of the digital assets stored in the compromised production computers? We formalize HD as a combinatorial optimization problem, prove its NP hardness, provide a near optimal algorithm (i.e., polynomial time approximation scheme). We also conduct simulations to show the impact of attacker capabilities.

Optimally Blending Honeypots into Production Networks: Hardness and Algorithms

TL;DR

The paper addresses optimal deployment of honeypots blended into production networks to maximize exposure of attackers to new attacks while limiting defender losses. It formalizes the Honeypot Deployment (HD) problem as a combinatorial optimization under budget and attacker reconnaissance uncertainty, and proves the decision version is NP-hard. The authors develop an exact dynamic-programming method and a Polynomial-Time Approximation Scheme (PTAS) to compute near-optimal honeypot placements, with runtime scaling polynomially in key parameters. They also conduct simulations to explore how attacker capabilities and risk attitudes affect effectiveness, showing the approach can significantly reduce expected losses under certain reconnaissance levels. The work provides a rigorous foundation for blending honeypots and offers practical algorithms for deployment in enterprise networks.

Abstract

Honeypot is an important cyber defense technique that can expose attackers new attacks. However, the effectiveness of honeypots has not been systematically investigated, beyond the rule of thumb that their effectiveness depends on how they are deployed. In this paper, we initiate a systematic study on characterizing the cybersecurity effectiveness of a new paradigm of deploying honeypots: blending honeypot computers (or IP addresses) into production computers. This leads to the following Honeypot Deployment (HD) problem, How should the defender blend honeypot computers into production computers to maximize the utility in forcing attackers to expose their new attacks while minimizing the loss to the defender in terms of the digital assets stored in the compromised production computers? We formalize HD as a combinatorial optimization problem, prove its NP hardness, provide a near optimal algorithm (i.e., polynomial time approximation scheme). We also conduct simulations to show the impact of attacker capabilities.
Paper Structure (15 sections, 5 theorems, 12 equations, 3 figures, 2 algorithms)

This paper contains 15 sections, 5 theorems, 12 equations, 3 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Hardness and Algorithmic Results
  4. Hardness Result
  5. Algorithmic results
  6. An exact algorithm via dynamic programming
  7. A Polynomial-Time Approximation Scheme (PTAS)
  8. Experiment
  9. Expected Losses under Different Attack Sequences
  10. Expected Loss w.r.t. Attacker's Reconnaissance Capability
  11. Limitations
  12. Related Work
  13. Conclusion
  14. Proof of Theorem \ref{['thm:np-h']}
  15. Proof of Lemma \ref{['lemma:dp']}

Key Result

theorem thmcountertheorem

The decision version of the HD problem is NP-complete.

Figures (3)

  • Figure 1: Illustrating the concepts of production, honeypot, and dummy computers and the idea of attack sequence.
  • Figure 2: Expected relative loss with respect to different risk-attitude (i.e., $\alpha=-0.05$ for most risk-seeking, $\alpha=-0.005$ for risk-seeking, $\alpha=0$ for risk-neutral, $\alpha=0.005$ for risk-averse, $\alpha=0.05$ for most risk-averse).
  • Figure 3: Attacker's reconnaissance capability vs. the expected loss to the defender.

Theorems & Definitions (10)

  • theorem thmcountertheorem
  • lemma thmcounterlemma: yao1980new
  • definition thmcounterdefinition
  • lemma thmcounterlemma
  • theorem thmcountertheorem
  • lemma thmcounterlemma
  • proof
  • proof : Proof of Theorem \ref{['thm:al2']}
  • proof : Proof of Theorem \ref{['thm:np-h']}
  • proof