The Third 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We call for original and unpublished papers, which must be formatted in the standard IEEE two-column format that is used by the IEEE ICC 2025 main conference, and must not exceed six pages in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 ICC 2025 workshop proceedings and IEEE Xplore. 

Please submit your papers in PDF format via edas https://edas.info/N33196

Important Dates

• Paper submission deadline: 20 January 2025 31 January 2025 

• Notification of acceptance: 10 March 2025 

• Camera-ready papers: 31 March 2025 

• Workshop date: 12 June 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

Program

Thursday, June-12 Full Day

09:00-09:15 Opening Session

9:30-10:30 Keynote 1

Prof Xianbin Wang, Western University, Canada

Intelligent Security, Privacy and Trust Provisioning in for 6G Enabled Systems

10:30-11:00 Coffee Break

11:10-12:30 Technical Session 1

Securing MIMO Wiretap Channel With DDPG-Based Friendly Jamming Under Non-Differentiable Channel

Task-Specific Trust Prediction with GNN for Minimized Risk of Task Completion in Dynamic Collaborative Systems

Deep Learning-Based Secure Content Fetching in UAV-Aided and RIS-Assisted Under Coordinated Aerial and Ground Eavesdropping

Contrasting Time-Frequency Representations for Unknown Waveform Detection

12:30-13:30 Lunch Break

13:30-14:30 Keynote 2

Prof Gunes Karabulut Kurt, Polytechnique Montréal, Canada

The Use of Deep Learning against Space Jamming Attacks

14:30-15:30 Technical Session 2

An Efficiency Clustering Approach based on Hedge Algebra in the IDS-based Machine Learning Models

A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks  

Explainable Machine Leaning-based False Data Injection Classification Framework for AVs

15:30-16:00 Coffee Break

16:30-17:30 Technical Session 3

RF Fingerprinting of Base Stations for Network Operator Identification

Data Diversity for a Channel-Resilient Training Database for Radio Frequency Fingerprint Identification

Transmitter Identification and Protocol Categorization in Shared Spectrum via Multi-Task RF Classification at the Network Edge