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arXiv Machine Learning By Nilesh Chakraborty, Petar Djukic, Burak Kantarci

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

Read the original on arXiv Machine Learning →

The paper introduces a threat model for malicious intent injection in AI‑native 6G networks and examines four injection strategies: stealth‑mode, random distribution, increasing frequency, and decreasing frequency. It proposes a dual‑path detection framework combining a CNN with TF‑IDF features for supervised detection and an AutoEncoder trained on benign data for one‑class detection. Evaluation shows the framework achieves higher accuracy (0.97) and F1‑score (0.98) than the state‑of‑the‑art baseline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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