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Strategic Deployment of Honeypots in Blockchain-based IoT Systems

Daniel Commey, Sena Hounsinou, Garth V. Crosby

TL;DR

This work tackles cybersecurity in Blockchain-based IoT systems by proposing an AI-powered IDS augmented with smart contracts to dynamically deploy honeypots. The defender–attacker interaction is modeled as a Bayesian game to analyze strategic honeypot deployment under incomplete information, deriving pure and mixed Bayesian Nash Equilibria. Simulations demonstrate that a dynamic, threat-driven honeypot strategy can outperform fixed deployments while balancing detection effectiveness and operational costs. The approach advances deception-based defense in BIoT and lays groundwork for autonomous, scalable security with threat-intelligence feedback loops.

Abstract

This paper addresses the challenge of enhancing cybersecurity in Blockchain-based Internet of Things (BIoTs) systems, which are increasingly vulnerable to sophisticated cyberattacks. It introduces an AI-powered system model for the dynamic deployment of honeypots, utilizing an Intrusion Detection System (IDS) integrated with smart contract functionalities on IoT nodes. This model enables the transformation of regular nodes into decoys in response to suspicious activities, thereby strengthening the security of BIoT networks. The paper analyses strategic interactions between potential attackers and the AI-enhanced IDS through a game-theoretic model, specifically Bayesian games. The model focuses on understanding and predicting sophisticated attacks that may initially appear normal, emphasizing strategic decision-making, optimized honeypot deployment, and adaptive strategies in response to evolving attack patterns.

Strategic Deployment of Honeypots in Blockchain-based IoT Systems

TL;DR

This work tackles cybersecurity in Blockchain-based IoT systems by proposing an AI-powered IDS augmented with smart contracts to dynamically deploy honeypots. The defender–attacker interaction is modeled as a Bayesian game to analyze strategic honeypot deployment under incomplete information, deriving pure and mixed Bayesian Nash Equilibria. Simulations demonstrate that a dynamic, threat-driven honeypot strategy can outperform fixed deployments while balancing detection effectiveness and operational costs. The approach advances deception-based defense in BIoT and lays groundwork for autonomous, scalable security with threat-intelligence feedback loops.

Abstract

This paper addresses the challenge of enhancing cybersecurity in Blockchain-based Internet of Things (BIoTs) systems, which are increasingly vulnerable to sophisticated cyberattacks. It introduces an AI-powered system model for the dynamic deployment of honeypots, utilizing an Intrusion Detection System (IDS) integrated with smart contract functionalities on IoT nodes. This model enables the transformation of regular nodes into decoys in response to suspicious activities, thereby strengthening the security of BIoT networks. The paper analyses strategic interactions between potential attackers and the AI-enhanced IDS through a game-theoretic model, specifically Bayesian games. The model focuses on understanding and predicting sophisticated attacks that may initially appear normal, emphasizing strategic decision-making, optimized honeypot deployment, and adaptive strategies in response to evolving attack patterns.
Paper Structure (15 sections, 7 equations, 2 figures, 3 tables)

This paper contains 15 sections, 7 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Proposed Model
  3. System Architecture
  4. Workflow
  5. Game Model
  6. Scenario Description
  7. One-shot Game
  8. Game Formulation
  9. Equilibrium Analysis
  10. Pure Strategy BNE
  11. Mixed Strategy BNE
  12. Simulation
  13. Simulation Setup and Implementation
  14. Simulation Experiments and Results
  15. Discussion and Conclusion

Figures (2)

  • Figure 1: System Model of BIoTs with IDS
  • Figure 2: Game tree for the BIoTs Game Model