← Back to all news
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.

Related stories

arXiv Machine Learning
Sep 17

A GAN-Based Framework for Robust DDoS Attack Detection

The paper introduces a GAN‑based framework for detecting DDoS attacks that are designed to evade traditional security systems. It combines Random Forests, Deep Neural Ensembles, and Transformer models trained on the CICDDoS2019 dataset with synthetic adversarial traffic generated by a WGAN‑GP. Experiments show that this hybrid training significantly improves detection accuracy and resilience against unseen adversarial traffic, and real‑world tests confirm its practical effectiveness.

By Makram Chehayeb, Walid Fahs, Amina Rizk, Rida Khatoun, Omran Berjawi
llmsdiffusionsafety
More like this →