# Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications

arXiv: https://arxiv.org/abs/1903.01563 · HTML: https://mlsanity.com/paper/1903.01563/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2019.

## Built on by (most cited first, 28 of 28)

- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network](https://mlsanity.com/paper/2101.12090/) (2021)
- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation](https://mlsanity.com/paper/2504.14541/) (2025)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond](https://mlsanity.com/paper/2103.13989/) (2021)
- [Jamming Attacks on Federated Learning in Wireless Networks](https://mlsanity.com/paper/2201.05172/) (2022)
- [Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning](https://mlsanity.com/paper/2107.12173/) (2021)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications](https://mlsanity.com/paper/2109.08139/) (2021)
- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)
- [Filtered Randomized Smoothing: A New Defense for Robust Modulation Classification](https://mlsanity.com/paper/2410.06339/) (2024)
- [On the benefits of robust models in modulation recognition](https://mlsanity.com/paper/2103.14977/) (2021)
- [SafeAMC: Adversarial training for robust modulation recognition models](https://mlsanity.com/paper/2105.13746/) (2021)
- [Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification](https://mlsanity.com/paper/2501.01620/) (2025)
- [Adapt under Attack and Domain Shift: Unified Adversarial Meta-Learning and Domain Adaptation for Robust Automatic Modulation Classification](https://mlsanity.com/paper/2511.01172/) (2025)
- [Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2512.12002/) (2025)
- [Real-time Over-the-air Adversarial Perturbations for Digital Communications using Deep Neural Networks](https://mlsanity.com/paper/2202.11197/) (2022)
- [Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training](https://mlsanity.com/paper/2206.06592/) (2022)
- [Universal Fourier Attack for Time Series](https://mlsanity.com/paper/2209.00757/) (2022)
- [Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2212.11778/) (2022)
- [AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery](https://mlsanity.com/paper/2309.16706/) (2023)
- [Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems](https://mlsanity.com/paper/2311.00207/) (2023)
- [Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2112.11414/) (2021)
- [Maximum Likelihood Distillation for Robust Modulation Classification](https://mlsanity.com/paper/2211.00748/) (2022)
- [Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack](https://mlsanity.com/paper/2410.01393/) (2024)
- [Real-World Adversarial Attacks on RF-Based Drone Detectors](https://mlsanity.com/paper/2512.20712/) (2025)
- [Adaptive Quantum-Safe Cryptography for 6G Vehicular Networks via Context-Aware Optimization](https://mlsanity.com/paper/2602.01342/) (2026)

## Abstract (arXiv)

Recent advancements in radio frequency machine learning (RFML) have demonstrated the use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet, deep learning techniques have been shown, in other applications, to be vulnerable to adversarial machine learning (ML) techniques, which seek to craft small perturbations that are added to the input to cause a misclassification. The current work differentiates the threats that adversarial ML poses to RFML systems based on where the attack is executed from: direct access to classifier input, synchronously transmitted over the air (OTA), or asynchronously transmitted from a separate device. Additionally, the current work develops a methodology for evaluating adversarial success in the context of wireless communications, where the primary metric of interest is bit error rate and not human perception, as is the case in image recognition. The methodology is demonstrated using the well known Fast Gradient Sign Method to evaluate the vulnerabilities of raw IQ based Automatic Modulation Classification and concludes RFML is vulnerable to adversarial examples, even in OTA attacks. However, RFML domain specific receiver effects, which would be encountered in an OTA attack, can present significant impairments to adversarial evasion.