On Identifying Adversarial Intent Injection in AI-Native 6G Networks
Dual-path CNN and AutoEncoder framework detects adversarial intent injection in AI-native 6G networks, reaching 0.97 accuracy and 0.98 F1.
The paper defines a fine-grained threat model for adversarial intent injection in AI-native 6G intent-based networking, where malicious policies are disguised within benign intent flows. It evaluates four injection strategies: stealth-mode, random distribution, increasing frequency, and decreasing frequency. A dual-path detection framework combines a CNN using TF-IDF features for supervised detection with an AutoEncoder trained only on benign data for one-class detection, reaching 0.97 accuracy and 0.98 F1-score, roughly 9% and 36% gains over the state-of-the-art baseline.
- Threat model covers stealth, random, and frequency-varying intent injection strategies
- CNN with TF-IDF handles supervised detection; AutoEncoder covers one-class detection
- 0.97 accuracy and 0.98 F1 exceed state-of-the-art baseline
From the original article
1 min read at arxiv.orgAI-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.
By Nilesh Chakraborty · arxiv.org
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