Distributed Intrusion Detection for Ultra-Mobile 6G Edge Architectures

Adedayo Bello

doi:10.62127/aijmr.2023.v01i01.1200

Distributed Intrusion Detection for Ultra-Mobile 6G Edge Architectures

Author(s)	Adedayo Bello
Country	United Kingdom
Abstract	The advent of sixth generation (6G) of wireless communications network is anticipated to bring an unprecedented level of movement, intelligence, and interconnections through ultra-dense edge computing, artificial intelligence (AI), and heterogeneous access technologies. Unlike generations that have come before it, 6G networks will include ultra-mobile environments where devices such as autonomous vehicles, unmanned aerial vehicles, extended reality platforms and cyber-physical systems are constantly changing network attachment points, while demanding ultra-low latency and high reliability. These characteristics make the network infrastructures considerably more complex when it comes to intrusions and detection. Traditional centralized intrusion detection systems (IDS) are no longer effective in such environments, due to the limited scalability, delayed response, and limited ability to provide contextual awareness. This research is concerned with distributed intrusion detection as a basic security mechanism for ultra-mobile 6G edge architectures. By decentralizing the logic of detecting these threats, and allowing for cooperative intelligence between edge nodes, distributed intrusion detection systems (D-IDS) are potentially able to offer in-time, scalable, and context-aware protection from new types of cyber threats. The article examines the shifting threat environment of edge environments that 6G will encounter, points to the shortcomings of centralized security concepts using edge environments and proposes a structured model for distributed intrusion detection adapted to ultra-mobile scenarios. Based on new research in edge computing, federated learning and AI secure, the study discusses how distributed IDS architectures help improve resilience and cut detection time and improve threat visibility while maintaining privacy. The article provides a useful and updated basis for future work and practical implementation of intrusion detection mechanisms for next-generation 6G networks.
Keywords	6G Networks; Distributed Intrusion Detection; Edge Computing; Ultra-Mobile Networks; Federated Learning; AI-Based Security; Network Defense.
Discipline	Engineering
Published In	Volume 1, Issue 1, July-August 2023
Published On	2023-08-10
DOI	https://doi.org/10.62127/aijmr.2023.v01i01.1200

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Distributed Intrusion Detection for Ultra-Mobile 6G Edge Architectures

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