The Sixth 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We call for original and unpublished papers, which must be formatted in the standard IEEE two-column format that is used by the IEEE Globecom 2026 main conference, and must not exceed six pages in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 2026 workshop proceedings and IEEE Xplore. 

Please submit your papers in PDF format via edas: https://edas.info/N35340

Important Dates

• Paper submission deadline: 12 August 2026

• Notification of acceptance: 20 September 2026

• Camera-ready papers: 30 September 2026

• Workshop date: 7 December 2026

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

• To be updated

Programs

• To be updated