The sweet taste of IoT deception: an adaptive honeypot framework for design and evaluation

Abstract

The rapid proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges for critical infrastructure systems. Honeypots and honeynets have emerged as promising deception technologies for detecting, deflecting, and investigating IoT-specific threats. In this paper, we propose an integrated framework for the design, implementation, and evaluation of adaptive honeypots in IoT environments. The framework consists of two key components: (1) an adaptive honeypot architecture that dynamically adjusts its behaviour based on observed attack patterns and (2) an evaluation methodology with quantitative metrics to assess the effectiveness of IoT honeypots. We discuss the current usage and future potential of this integrated framework in the context of critical infrastructure protection, highlighting challenges and opportunities for collaborative defence against evolving cyber threats.

Keywords

• adaptive honeypots
• IoT security
• deception technology
• machine learning
• intrusion detection
• evaluation metrics