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

In conjunction with IEEE Global Communications Conference (GLOBECOM) 2025, 8-12 December 2025, Taipei, Taiwan

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We call for original and unpublished papers, which must be formatted in the standard IEEE two-column format that is used by the IEEE Globecom 2025 main conference, and must not exceed six pages in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://gcwkshps2025-mldlws.edas.info/

Important Dates

Paper submission deadline: 15 July 2025
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