Adaptive Honeypot Allocation in Multi-Attacker Networks via Bayesian Stackelberg Games

2025-05-21

AI Summary

This work addresses the optimal honeypot deployment problem in large-scale directed networks under resource constraints and concurrent multi-adversary threats in dynamic lateral movement scenarios. We propose the first Bayesian Stackelberg game framework tailored for multiple attackers, integrating heterogeneous attacker profiling with an intrusion detection system (IDS)-driven dynamic belief updating mechanism to enable online, adaptive defense optimization. Our method combines multi-follower Stackelberg equilibrium computation, dynamic probabilistic inference, and compact integer programming to achieve millisecond-scale decision-making. Evaluated on a real-scale network with 500 nodes and over 1,500 edges, it effectively disrupts coordinated lateral movement within several rounds, significantly outperforming static deployment and single-attacker baseline approaches. Key contributions include: (i) theoretical modeling innovation—multi-attacker Bayesian Stackelberg games; (ii) mechanism design advancement—IDS-feedback-driven belief evolution; and (iii) empirical validation of engineering scalability.

Technology Category

• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Adversarial Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements

Abstract

Defending against sophisticated cyber threats demands strategic allocation of limited security resources across complex network infrastructures. When the defender has limited defensive resources, the complexity of coordinating honeypot placements across hundreds of nodes grows exponentially. In this paper, we present a multi-attacker Bayesian Stackelberg framework modeling concurrent adversaries attempting to breach a directed network of system components. Our approach uniquely characterizes each adversary through distinct target preferences, exploit capabilities, and associated costs, while enabling defenders to strategically deploy honeypots at critical network positions. By integrating a multi-follower Stackelberg formulation with dynamic Bayesian belief updates, our framework allows defenders to continuously refine their understanding of attacker intentions based on actions detected through Intrusion Detection Systems (IDS). Experimental results show that the proposed method prevents attack success within a few rounds and scales well up to networks of 500 nodes with more than 1,500 edges, maintaining tractable run times.

Problem

• Strategic allocation of limited security resources in complex networks
• Coordinating honeypot placements across hundreds of nodes efficiently
• Modeling concurrent adversaries with distinct preferences and capabilities

Innovation

• Bayesian Stackelberg framework for multi-attacker scenarios
• Dynamic Bayesian belief updates for attacker intention analysis
• Strategic honeypot deployment at critical network nodes