论AI原生6G网络中的对抗性意图注入识别
On Identifying Adversarial Intent Injection in AI-Native 6G Networks
- University of Ottawa（渥太华大学）
- Nokia Bell Labs（诺基亚贝尔实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对AI原生6G网络中恶意意图注入攻击，提出双路径检测框架（CNN与AutoEncoder），准确率达0.97，F1达0.98，优于基线。
AI中文摘要：
AI原生6G网络将基于意图的网络管理（IBN）推至前沿，使得高层目标能够被转化为网络配置。然而，这种抽象化引入了新的攻击面，主要是对抗性意图注入，即恶意策略被伪装在良性意图流中。如果 adversaries 采用隐蔽模式的恶意意图注入，攻击实例的检测可能会变得显著更加困难。鉴于这些情况，我们首先定义了一个细粒度的威胁模型，以促进AI原生网络中恶意意图注入威胁的应对。同时，我们研究了四种恶意意图注入策略——隐蔽模式、随机分布、频率递增和频率递减——并提出了一种双路径检测框架：（i）使用TF-IDF特征的CNN用于监督式恶意意图检测，以及（ii）仅使用良性数据训练的AutoEncoder用于单类恶意意图检测。我们的评估展示了强大的检测性能，准确率提升至0.97（约9%的提升），F1分数提升至0.98（约36%的提升），均优于现有最先进的基线。
英文摘要：
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.
