LATIDIA · Ciberseguridad

Sobre la identificación de la inyección de intención adversarial en redes 6G nativas de IA

arXiv:2609.12144v1 Anuncio Tipo: cruzado Resumen: Las redes 6G nativas de IA han puesto a la red basada en intenciones (IBN) en primer plano, lo que permite que los objetivos de alto nivel se traduzcan en configuraciones de red. Sin embargo, esto es un

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arXiv:2609.12144v1 Announce Type: cross Abstract: AI-native 6G networks have brought Intent-Based Networking (IBN) to the forefront, enabling high-level goals to be translated into network configurations. However, this abstraction opens new attack surfaces, primarily adversarial intent injection, where malicious policies are disguised within benign intent flows. The detection of attack instances might become significantly more difficult if the adversaries adopt a stealthy mode of malicious intent injection. With all these in mind, we first define a fine-grained threat model that facilitates the threat of malicious intent injection in an AI-native network. Alongside, we investigate four malicious intent injection strategies$-$ stealth-mode, random distribution, increasing frequency, and decreasing frequency- and propose a dual-path