# Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering xApp

arXiv: https://arxiv.org/abs/2505.01816 · HTML: https://mlsanity.com/paper/2505.01816/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 74 references that are in the map.
- First posted on arXiv 3 May 2025.

## Builds on (most cited first, 24 of 24)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Ablation Studies in Artificial Neural Networks](https://mlsanity.com/paper/1901.08644/) (2019)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data](https://mlsanity.com/paper/2204.06701/) (2022)
- [Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead](https://mlsanity.com/paper/2005.10027/) (2020)
- [Intelligent O-RAN for Beyond 5G and 6G Wireless Networks](https://mlsanity.com/paper/2005.08374/) (2020)
- [A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions](https://mlsanity.com/paper/1708.02562/) (2017)
- [A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches](https://mlsanity.com/paper/2208.09125/) (2022)
- [Implementing and Evaluating Security in O-RAN: Interfaces, Intelligence, and Platforms](https://mlsanity.com/paper/2304.11125/) (2023)
- [Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems](https://mlsanity.com/paper/2107.01806/) (2021)
- [Open RAN Security: Challenges and Opportunities](https://mlsanity.com/paper/2212.01510/) (2022)
- [Machine Learning-based Early Attack Detection Using Open RAN Intelligent Controller](https://mlsanity.com/paper/2302.01864/) (2023)
- [Experimental Study of Adversarial Attacks on ML-based xApps in O-RAN](https://mlsanity.com/paper/2309.03844/) (2023)
- [Evaluating the Security of Open Radio Access Networks](https://mlsanity.com/paper/2201.06080/) (2022)
- [Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)](https://mlsanity.com/paper/2201.06093/) (2022)
- [Cross-Domain AI for Early Attack Detection and Defense Against Malicious Flows in O-RAN](https://mlsanity.com/paper/2401.09204/) (2024)
- [From Centralized RAN to Open RAN: A Survey on the Evolution of Distributed Antenna Systems](https://mlsanity.com/paper/2411.12166/) (2024)
- [Best Operator Policy in a Heterogeneous Wireless Network](https://mlsanity.com/paper/1408.4992/) (2014)
- [OSC Community Lab: The Integration Test Bed for O-RAN Software Community](https://mlsanity.com/paper/2208.14885/) (2022)
- [Enhancing O-RAN Security: Evasion Attacks and Robust Defenses for Graph Reinforcement Learning-based Connection Management](https://mlsanity.com/paper/2405.03891/) (2024)

## Built on by (most cited first, 2 of 2)

- [Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability (Technical Report)](https://mlsanity.com/paper/2512.14742/) (2025)
- [Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery](https://mlsanity.com/paper/2604.01049/) (2026)

## Abstract (arXiv)

The Open Radio Access Network (O-RAN) architecture is revolutionizing cellular networks with its open, multi-vendor design and AI-driven management, aiming to enhance flexibility and reduce costs. Although it has many advantages, O-RAN is not threat-free. While previous studies have mainly examined vulnerabilities arising from O-RAN's intelligent components, this paper is the first to focus on the security challenges and vulnerabilities introduced by transitioning from single-operator to multi-operator RAN architectures. This shift increases the risk of untrusted third-party operators managing different parts of the network. To explore these vulnerabilities and their potential mitigation, we developed an open-access testbed environment that integrates a wireless network simulator with the official O-RAN Software Community (OSC) RAN intelligent component (RIC) cluster. This environment enables realistic, live data collection and serves as a platform for demonstrating APATE (adversarial perturbation against traffic efficiency), an evasion attack in which a malicious cell manipulates its reported key performance indicators (KPIs) and deceives the O-RAN traffic steering to gain unfair allocations of user equipment (UE). To ensure that O-RAN's legitimate activity continues, we introduce MARRS (monitoring adversarial RAN reports), a detection framework based on a long-short term memory (LSTM) autoencoder (AE) that learns contextual features across the network to monitor malicious telemetry (also demonstrated in our testbed). Our evaluation showed that by executing APATE, an attacker can obtain a 248.5% greater UE allocation than it was supposed to in a benign scenario. In addition, the MARRS detection method was also shown to successfully classify malicious cell activity, achieving accuracy of 99.2% and an F1 score of 0.978.