When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions
Not on the map. Why?
Abstract (arXiv)
Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium. To support both attack and defense strategies, machine learning (ML) provides automated means to learn from and adapt to wireless communication characteristics that are hard to capture by hand-crafted features and models. This article discusses motivation, background, and scope of research efforts that bridge ML and wireless security. Motivated by research directions surveyed in the context of ML for wireless security, ML-based attack and defense solutions and emerging adversarial ML techniques in the wireless domain are identified along with a roadmap to foster research efforts in bridging ML and wireless security.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 6; 2022: 3; 2023: 2; 2024: 1.
12 papers.
- 2021Adversarial Machine Learning for 5G Communications Securitycited by 12
- 2021Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyondcited by 6
- 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
- 2022Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examplescited by 5
- 2021Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learningcited by 4
- 2021Adversarial Attacks against Deep Learning Based Power Control in Wireless Communicationscited by 4
- 2023Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networkscited by 4
- 2021Adversarial Reinforcement Learning in Dynamic Channel Access and Power Controlcited by 1
- 2022Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learningcited by 1
- 2023Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learningcited by 1
- 2024Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learningcited by 1
- 2021Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfacescited by 0
- 2024Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning30 Dec 2024
- 2023Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning27 Dec 2023
- 2023Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks12 Jan 2023
- 2022Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning22 Dec 2022
- 2022Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples4 Jul 2022
- 2022Jamming Attacks on Federated Learning in Wireless Networks13 Jan 2022
- 2021Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces21 Dec 2021
- 2021Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications16 Sep 2021
- 2021Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning22 Jul 2021
- 2021Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control12 May 2021
- 2021Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond25 Mar 2021
- 2021Adversarial Machine Learning for 5G Communications Security7 Jan 2021