AI detects hidden malicious commands in 6G network setups
On Identifying Adversarial Intent Injection in AI-Native 6G Networks
Networking and Internet ArchitectureCryptography and SecurityMachine Learning
Summary
Networks that use AI to set themselves up can be tricked by bad commands hidden inside normal ones. This paper explains how attackers might secretly insert these harmful commands to disrupt the system. The authors created a method that uses two AI tools to spot when these sneaky attacks happen. Their approach catches threats better than existing techniques, making future networks safer.
What this means in practice
- •For network security teams: Detect stealthy malicious command injections in AI-driven 6G networks to enhance threat monitoring.
- •For telecommunications engineers: Incorporate dual-path AI detection systems to prevent hidden policy attacks during network configuration in 6G infrastructures.
Authors
Nilesh Chakraborty, Petar Djukic, Burak Kantarci
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.