# Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing

arXiv: https://arxiv.org/abs/2101.08724 · HTML: https://mlsanity.com/paper/2101.08724/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 25 references that are in the map.
- First posted on arXiv 21 Jan 2021.

## Builds on (most cited first, 18 of 18)

- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Deep Learning for Wireless Communications](https://mlsanity.com/paper/2005.06068/) (2020)
- [Deep Learning for Launching and Mitigating Wireless Jamming Attacks](https://mlsanity.com/paper/1807.02567/) (2018)
- [Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers](https://mlsanity.com/paper/2005.05321/) (2020)
- [Deep Reinforcement Learning for Resource Management in Network Slicing](https://mlsanity.com/paper/1805.06591/) (2018)
- [Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks](https://mlsanity.com/paper/1911.00500/) (2019)
- [The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection](https://mlsanity.com/paper/1902.10674/) (2019)
- [Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels](https://mlsanity.com/paper/2002.02400/) (2020)
- [Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning](https://mlsanity.com/paper/1910.10766/) (2019)
- [Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers](https://mlsanity.com/paper/2006.14576/) (2020)
- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers](https://mlsanity.com/paper/2007.16204/) (2020)
- [How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning](https://mlsanity.com/paper/2101.05768/) (2021)
- [How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy](https://mlsanity.com/paper/2005.07675/) (2020)
- [Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics](https://mlsanity.com/paper/1908.03242/) (2019)
- [Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing](https://mlsanity.com/paper/2009.06579/) (2020)
- [Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities](https://mlsanity.com/paper/2010.09916/) (2020)
- [The Slice Is Served: Enforcing Radio Access Network Slicing in Virtualized 5G Systems](https://mlsanity.com/paper/1902.00389/) (2019)

## Built on by (most cited first, 6 of 6)

- [Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond](https://mlsanity.com/paper/2103.13989/) (2021)
- [Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning](https://mlsanity.com/paper/2107.12173/) (2021)
- [Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)](https://mlsanity.com/paper/2201.06093/) (2022)
- [The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions](https://mlsanity.com/paper/2304.09240/) (2023)
- [Integrated LLM-Based Intrusion Detection with Secure Slicing xApp for Securing O-RAN-Enabled Wireless Network Deployments](https://mlsanity.com/paper/2504.00341/) (2025)
- [Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack](https://mlsanity.com/paper/2410.01393/) (2024)

## Abstract (arXiv)

Network slicing manages network resources as virtual resource blocks (RBs) for the 5G Radio Access Network (RAN). Each communication request comes with quality of experience (QoE) requirements such as throughput and latency/deadline, which can be met by assigning RBs, communication power, and processing power to the request. For a completed request, the achieved reward is measured by the weight (priority) of this request. Then, the reward is maximized over time by allocating resources, e.g., with reinforcement learning (RL). In this paper, we introduce a novel flooding attack on 5G network slicing, where an adversary generates fake network slicing requests to consume the 5G RAN resources that would be otherwise available to real requests. The adversary observes the spectrum and builds a surrogate model on the network slicing algorithm through RL that decides on how to craft fake requests to minimize the reward of real requests over time. We show that the portion of the reward achieved by real requests may be much less than the reward that would be achieved when there was no attack. We also show that this flooding attack is more effective than other benchmark attacks such as random fake requests and fake requests with the minimum resource requirement (lowest QoE requirement). Fake requests may be detected due to their fixed weight. As an attack enhancement, we present schemes to randomize weights of fake requests and show that it is still possible to reduce the reward of real requests while maintaining the balance on weight distributions.