On Identifying Adversarial Intent Injection in AI-Native 6G Networks

2026-09-10

AI Summary

研究针对AI原生6G网络中的敌意注入问题，定义了威胁模型并提出了一种结合CNN与自编码器的双路径检测框架以有效识别恶意意图。

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 detection framework: (i) a CNN using TF-IDF features for supervised malicious intent detection, and (ii) an AutoEncoder trained exclusively on benign data for one-class malicious intent detection. Our evaluation demonstrates strong detection performance, with accuracy improving to 0.97 (~9\% gain) and F1-score to 0.98 (~36\% gain) over the state-of-the-art baseline.

Problem

• Adversarial Intent Injection
• AI-native 6G Networks
• Intent-Based Networking (IBN)
• Threat Model

Innovation

• dual-path detection framework
• malicious intent injection
• AI-native 6G networks
• CNN with TF-IDF features
• AutoEncoder for one-class detection