ACM Workshop on Wireless Security and Machine Learning (WiseML 2025)

The ACM Workshop on Wireless Security and Machine Learning (WiseML) 2025 will be held in conjunction with the ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) 2025. Accepted, registered, and presented papers will appear in the conference proceedings and the ACM digital library.

Scope and background

Machine learning (ML) has become a powerful tool for analyzing spectrum data and developing efficient and secure solutions for IoT, CPS, 5G, NextG, and other emerging communication systems. However, recent research highlights how adversarial ML (AML) techniques can compromise the performance of ML-based wireless systems, underscoring the urgent need to understand and mitigate AML’s impact on wireless technologies.

Developing efficient and robust ML algorithms for wireless security is essential, particularly in environments constrained by power and computational resources. Such advancements are critical to ensuring the integrity and reliability of wireless communications. There is an increasing demand to explore the intersection of ML with wireless security, privacy, and robustness to address these challenges effectively.

To advance this field, our workshop aims to unite researchers and practitioners from the ML, privacy, security, wireless communications, and networking communities worldwide. The workshop provides a collaborative platform for sharing cutting-edge research, exchanging ideas, and fostering partnerships to push the boundaries of knowledge in these vital and rapidly evolving domains.

Topics of Interest (but not limited to)

Adversarial ML Techniques

  • Adversarial examples
  • Adversarial reinforcement learning
  • Defense techniques
  • Generative adversarial learning
  • Poisoning attacks
  • Smart jamming, spoofing, and mitigation
  • Trojan/backdoor attacks

Privacy & Security Issues of ML Solutions

  • Differential privacy and alternative privacy models
  • Generative AI (GenAI) security
  • Information-theoretic privacy
  • Large language models (LLM) security
  • Membership inference attacks
  • Model inversion
  • Model extraction
  • Machine unlearning
  • Physical layer privacy
  • Privacy-preserving learning
  • Secure learning

ML Applications

  • 5G/NextG/cloud security
  • Access control
  • Anonymity
  • Cognitive radio
  • Covert communications
  • Device identification/ RF fingerprinting
  • Digital twin security
  • Explainable ML for trusted security
  • Integrated sensing and communication (ISAC) security
  • Intrusion detection
  • Localization
  • Wireless sensing
  • Network virtualization
  • O-RAN security
  • Security for mobile autonomous multi-agent platforms
  • Semantic and task-oriented communications

Strengthening ML Solutions

  • Authentication
  • Certified defense
  • Correcting for model or data drift
  • Cyber-physical systems/IoT
  • Data augmentation
  • Datasets
  • Efficient and edge deployable solutions
  • Embedded computing
  • Experiments and testbeds
  • Federated learning
  • Hardware solutions
  • Information discovery
  • Lifelong learning
  • Uncertainty quantification

Workshop Chairs

Danda B. Rawat
Howard University, USA
Washington D.C, USA
Yalin Sagduyu
Nexcepta, USA
Gaithersburg, MD, USA
Yi Shi
Virginia Tech
Arlington, VA, USA
Xuyu Wang
Florida International University
Miami, FL, USA

Information Systems (HotCRP) Chair

  • Shanghao Shi, Virginia Tech, VA, USA

Steering Committee

  • Dr. Wenjing Lou, Virginia Tech, VA, USA
  • Dr. Sennur Ulukus, University of Maryland, MD, USA
  • Dr. K.P. (Suba) Subbalakshmi, Stevens Institute of Technology, NJ, USA
  • Dr. Aylin Yener, The Ohio State University, OH, USA

Technical Program Committee (TPC) Members:

  • Eyuphan Bulut, Virginia Commonwealth University, USA
  • M. Cenk Gursoy, Syracuse University, USA
  • Jacek Kibilda, Virginia Tech, USA
  • Silvija Kokalj-Filipovic, Rowan University, USA
  • Marwan Krunz, Univeristy of Arizona, USA
  • Zhuo Lu, University of South Florida, USA
  • Javier Parra-Arnau, Universitat Politècnica de Catalunya, Spain
  • Heejun Roh, Inha University, Korea
  • Dola Saha, SUNY Albany, USA
  • Sachin Shetty, Old DOminion University, USA
  • Lei Shi, Hefei University of Technology, China
  • Haijian Sun, University of Georgia, USA
  • Ayse Ünsal, EURECOM, France
  • Ning Wang, University of South Florida, USA
  • Diana-Alexandra Vasile, Nokia Bell Labs Cambridge, UK
  • Feng Ye, University of Wisconcin-Madison
  • Junqing Zhang, University of Liverpool, UK

Submission Guidelines

Submission site: https://wiseml25.hotcrp.com/.

Workshop papers must be written in English, must be formatted in the standard ACM conference style, and are not to exceed six pages. Accepted papers will appear in the conference proceedings and the ACM digital library.

Only PDF files will be accepted for the review process. All papers must be thoroughly anonymized for double-blind reviewing.

Important Dates:

  • Paper Submission Deadline: March 28, 2025
  • Acceptance Notification: April 25, 2025
  • Camera-Ready Paper Submission: May 12, 2025
  • Workshop Event: July 3, 2025