Honeypot Systems
Akshay Mudgal · 2026
This chapter presents honeypots as a groundbreaking defense approach that proactively misleads attackers instead of simply responding to intrusions. Honeypots, crafted to seem susceptible, attract opponents into managed settings where all interactions are observed and examined. Their power comes from exceptional signal clarity: Any traffic aimed at a honeypot is by nature malicious, providing very dependable intelligence and minimizing false positives. The chapter categorizes honeypots as low-, medium-, and high-interaction systems, with each type balancing realism, resource requirements, and depth of intelligence. Low-interaction traps identify scanning behavior, medium-level systems record adversarial techniques, and high-interaction honeypots facilitate thorough behavioral examination, though they involve increased risk and complexity. Honeynets and hybrid honeypot architectures are presented as scalable frameworks that merge various deception layers, frequently combined with machine learning and threat intelligence systems. In addition to detection, honeypots act as instruments for adversarial profiling, forensic investigation, and model training, offering ground-truth data seldom accessible through alternative approaches. Despite issues related to operational burden, realism, and ethical concerns, the chapter contends that honeypots can shift defense from a reactive stance to proactive intelligence-based security.