AI Adoption in Pipeline Security Monitoring: A Systematic Review of AIoT-Driven Honey Frameworks

As time goes by, it is undeniable that we are living in an era marked by the increasing convergence of Artificial Intelligence and the Internet of Things (AIoT) within industrial infrastructures, leading to new security vulnerabilities specifically in critical pipeline systems. Concurrently, deception-based defense mechanisms such as honeypots have shown strong potential in mitigating advanced cyber threats, presenting new opportunities to strengthen the security of such infrastructures. This paper presents a comprehensive review and thematic analysis of recent approaches that integrate AIoT and honeypot technologies to protect pipelines and related critical infrastructure. By surveying research published between 2019 and 2024, we categorize current frameworks based on architecture type, detection methodology, deployment environment and infrastructure domain. The objective of this study is to identify trends, highlight the underutilization of distribute AI frameworks and address the lack of context-aware threat adaptation in existing solutions. By synthesizing current contributions and research gaps, this review provides a structured understanding of the infrastructure protection. The results aim to inform future research directions and guide the development of resilient, intelligent security frameworks tailored to national pipeline systems.