The rapid proliferation of Internet of Things (IoT) ecosystems, coupled with the emergence of next-generation communication infrastructures such as 6G, has significantly transformed the digital landscape. While these developments have enabled unprecedented levels of connectivity, automation, and real-time decision making, they have also introduced complex cybersecurity vulnerabilities across distributed network environments. Traditional centralized security architectures are increasingly inadequate for protecting highly distributed and latency-sensitive systems. In response, edge intelligence—an emerging paradigm integrating artificial intelligence capabilities with edge computing infrastructure—has gained attention as a viable solution for decentralized threat detection and network protection. This research investigates the role of edge intelligence in enhancing intrusion detection mechanisms within IoT-enabled networks operating in next-generation communication environments.

The study synthesizes theoretical insights from contemporary research on edge computing, distributed artificial intelligence, network security, and intrusion detection systems to construct a conceptual framework for intelligent security at the network edge. Particular emphasis is placed on the integration of collaborative deep learning inference, anomaly-based detection models, and device-edge cooperation strategies that enable real-time analysis of network traffic patterns. Additionally, the study examines the impact of realistic attack datasets, including distributed denial-of-service scenarios, on the design of robust detection systems.

Methodologically, the research develops a descriptive analytical model based on device-edge cooperative inference and adaptive optimization techniques for distributed learning. The analysis explores how intelligent edge nodes can process multivariate time-series data generated by IoT devices, identify abnormal behavioral patterns, and respond to cyber threats without relying heavily on centralized cloud resources. The results indicate that edge-based intrusion detection significantly reduces response latency, improves scalability, and enhances privacy protection while maintaining high detection accuracy in dynamic network environments.

The findings highlight the transformative potential of edge intelligence for securing IoT networks in future communication infrastructures. However, challenges related to model training, data heterogeneity, interoperability standards, and resource limitations remain significant obstacles. The study concludes by outlining future research directions focusing on federated learning frameworks, cross-domain standardization, and intelligent network orchestration to enable resilient and adaptive cybersecurity architectures in large-scale distributed systems.