The Fourth Workshop on Machine Learning and Deep Learning for Wireless Security 

Submission Instruction

We call for original and unpublished papers, which must be formatted in the standard IEEE two-column format that is used by the IEEE Globecom 2025 main conference, and must not exceed six pages in length (including references). All submitted papers will go through a strict peer review process, and all accepted papers that are presented by one of the authors at the workshop will be published in the IEEE Globecom 2025 workshop proceedings and IEEE Xplore. 

Please submit your papers in PDF format via edas: https://edas.info/N34076

Important Dates

• Paper submission deadline: 15 July 2025 7 August 2025 (FIRM)

• Notification of acceptance: 1 September 2025

• Camera-ready papers: 1 October 2025

• Workshop date: 8 December 2025

Workshop Scope

Deep learning has transformed many areas including the wireless security and privacy domains. It has significantly strengthened the design of security approaches, attacks as well as the defence to the Internet of Things (IoT), beyond 5G/6G, from the physical layer to the upper layers. This workshop aims to bring together practitioners and researchers from both academia and industry for discussion and technical presentations on fundamental and practically relevant questions related to many challenges arising from deep learning-based security and privacy for wireless communications and networking. It also aims to provide the industry with fresh insight into the development of machine learning and deep learning applications in wireless security. 

Topics of Interests

In line with such objectives, original contributions are solicited on topics of interest to include, but not limited to, the following: 

• Artificial intelligence-generated content (AIGC) for wireless security 

• Large language model (LLM) for wireless security 

• Machine learning/deep learning-driven device identification using radio frequency fingerprint, physical layer channel features, and network traffic features 

• Deep learning-enhanced physical layer security 

• Deep learning-enhanced RF security 

• Adversarial machine learning in wireless communications, including adversarial erosion attacks, poisoning attacks, and Trajon/backdoor attacks 

• Defensive and anticipatory aspects of adversarial machine learning in wireless communications 

• Security and privacy of deep learning-based wireless sensing 

• Intrusion and anomaly detection for wireless networks 

• Prototype, practical testbeds, and performance evaluation 

Organization Team

Steering Committee

General Chairs

TPC Chairs

Speakers

• Professor Rafael Schaefer 

• Title: Building Trustworthy Communications Networks: AI in 6G PHY

• Bio: Rafael Schaefer (Senior Member, IEEE) received the Dipl.-Ing. degree in electrical engineering and computer science from the Technische Universität Berlin, Germany, in 2007, and the Dr.-Ing. degree in electrical engineering from the Technische Universität München, Germany, in 2012. He is a Professor and the Head of the Chair of Information Theory and Machine Learning, Technische Universität Dresden, Germany. Since 2023, he is also leading the Wireless Connectivity and Sensing Group at the Barkhausen Institut, Dresden, Germany. From 2013 to 2015, he was a Post-Doctoral Research Fellow with Princeton University. From 2015 to 2020, he was an Assistant Professor with the Technische Universität Berlin. From 2021 to 2022, he was a Professor with the Universität Siegen. Among his publications is the book Information Theoretic Security and Privacy of Information Systems (Cambridge University Press, 2017). He was a recipient of the VDE Johann-Philipp-Reis Award in 2013. He received the Joy Thomas Tutorial Paper Award in 2025 and Best Paper Awards from the German Information Technology Society (ITG-Preis) in 2016 and IEEE Global Communications Conference in 2023.

Programs

8:30 AM – 12:00 PM Monday, 8 December

Keynote 

08:40-09:40

Professor Rafael Schaefer, Technische Universität Dresden, Germany 

Title: Building Trustworthy Communications Networks: AI in 6G PHY 

Technical Session I

09:40 - 09:58

MTL4UAV: A Multi-Task Learning Framework for UAV Attack Detection and Classification
Kensley Benjamin (University of the District of Columbia, USA); Thabet Kacem (University of the District of Columbia, USA); Cagatay Catal (Qatar University, Qatar)

Coffee Break

10:00-10:30

Technical Session 2

10:30-12:00

Physical-Layer Security in OFDM-Based PNC-Enabled IoT: Analysis and Detection of Symbol-Level Jamming Attacks
Ehsan Atefat Doost (Instituto de Telecomunicações & Universidade de Aveiro, Portugal); Georgios Mantas (Instituto de Telecomunicações - Pólo de Aveiro, Portugal); Joaquim Bastos (Instituto de Telecomunicações, Portugal); Jonathan Rodriguez (Instituto de Telecomunicações, Portugal); Anastasia Tsiota (Fogus Innovations and Services, Greece); Dionysis Xenakis (National and Kapodistrian University of Athens, Greece)

Neural Estimation of Information Leakage for Secure Communication System Design
Darius S. Heerklotz (TU Brausschweig, Germany); Ingo Schröder (TU Braunschweig, Germany); Pin-Hsun Lin (Technische Universität Braunschweig, Germany); Christian Deppe (Technical University of Braunschweig, Germany); Eduard A Jorswieck (Technische Universität Braunschweig, Germany)

Social and Physical Attributes-Defined Trust Evaluation for Effective Collaborator Selection in Human-Device Coexistence Systems
Botao Zhu (University of Western Ontario, Canada); Xianbin Wang (University of Western Ontario, Canada)

MuKD: Multi-Stream CNN and Knowledge Distillation for Behavioral Ransomware Detection
Nattapol Chiewnawintawat (National Chung Cheng University, Taiwan); Nopparuj Suetrong (Chiang Mai University, Thailand); Van-Linh Nguyen (National Chung Cheng University & College of Engineering, Taiwan)

A Digital Twin Platform for QoS Optimization Under DoS Attacks for Next Generation Radio Networks
Mehmet Ali Ali Erturk (Edinburgh Napier University, United Kingdom (Great Britain)); Kubra Duran (Edinburgh Napier University, United Kingdom (Great Britain)); Ahmed Y Al-Dubai (Edinburgh Napier University, United Kingdom (Great Britain)); Berk Canberk (Edinburgh Napier University, United Kingdom (Great Britain))