# SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network

arXiv: https://arxiv.org/abs/2602.03596 · HTML: https://mlsanity.com/paper/2602.03596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [SoK: Decoding the Enigma of Encrypted Network Traffic Classifiers](https://mlsanity.com/paper/2503.20093/) (2025)
- [Machine Learning based Anomaly Detection for 5G Networks](https://mlsanity.com/paper/2003.03474/) (2020)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Feature Extraction for Novelty Detection in Network Traffic](https://mlsanity.com/paper/2006.16993/) (2020)

## Abstract (arXiv)

Machine learning-based anomaly detection systems are increasingly being adopted in 5G Core networks to monitor complex, high-volume traffic. However, most existing approaches are evaluated under strong assumptions that rarely hold in operational environments, notably the availability of independent and identically distributed (IID) data and the absence of adaptive attackers. In this work, we study the problem of detecting 5G attacks in the wild, focusing on realistic deployment settings. We propose a set of Security-Aware Guidelines for Evaluating anomaly detectors in 5G Core Network (SAGE-5GC), driven by domain knowledge and consideration of potential adversarial threats. Using a realistic 5G Core dataset, we first train several anomaly detectors and assess their baseline performance against standard 5GC control-plane cyberattacks targeting PFCP-based network services. We then extend the evaluation to adversarial settings, where an attacker tries to manipulate the observable features of the network traffic to evade detection, under the constraint that the intended functionality of the malicious traffic is preserved. Starting from a selected set of controllable features, we analyze model sensitivity and adversarial robustness through randomized perturbations. Finally, we introduce a practical optimization strategy based on genetic algorithms that operates exclusively on attacker-controllable features and does not require prior knowledge of the underlying detection model. Our experimental results show that adversarially crafted attacks can substantially degrade detection performance, underscoring the need for robust, security-aware evaluation methodologies for anomaly detection in 5G networks deployed in the wild.