AI-Driven Fuzzing for Vulnerability Assessment of 5G Traffic Steering Algorithms

TL;DR

This paper introduces an AI-driven fuzz testing framework using NSGA-II to systematically identify vulnerabilities in 5G Traffic Steering algorithms, outperforming traditional methods in detecting critical failures and enhancing network robustness.

Contribution

It presents a novel AI-based fuzzing approach tailored for 5G TS algorithms, improving vulnerability detection and robustness validation.

Findings

Detects 34.2% more vulnerabilities than traditional testing.

Identifies 5.8% more critical failures.

Demonstrates effectiveness across multiple scenarios.

Abstract

Traffic Steering (TS) dynamically allocates user traffic across cells to enhance Quality of Experience (QoE), load balance, and spectrum efficiency in 5G networks. However, TS algorithms remain vulnerable to adversarial conditions such as interference spikes, handover storms, and localized outages. To address this, an AI-driven fuzz testing framework based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to systematically expose hidden vulnerabilities. Using NVIDIA Sionna, five TS algorithms are evaluated across six scenarios. Results show that AI-driven fuzzing detects 34.2% more total vulnerabilities and 5.8% more critical failures than traditional testing, achieving superior diversity and edge-case discovery. The observed variance in critical failure detection underscores the stochastic nature of rare vulnerabilities. These findings demonstrate that AI-driven fuzzing offers an effective and scalable validation approach for improving TS algorithm robustness and ensuring resilient 6G-ready networks.