ACM Workshop on Wireless Security and Machine Learning (WiseML 2024)

The ACM Workshop on Wireless Security and Machine Learning (WiseML) 2024 will be held in conjunction with the ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) 2024. Accepted, registered, and presented papers will appear in the conference proceedings and the ACM digital library.

Scope and background

Machine learning (ML) has emerged as a viable solution for effectively learning from spectrum data, addressing complex tasks in IoT, 5G, and beyond, and ensuring the security of emerging communication systems against adversaries. Recent research has highlighted the effectiveness of adversarial ML (AML) techniques in negatively impacting the performance of ML-based wireless systems, emphasizing the need for a deeper understanding of the impact of AML on wireless technologies.

Simultaneously, the widespread use of wireless devices operating with diverse communication technologies in heterogeneous spectrum environments has rendered them susceptible targets to various attacks. It is crucial to harness efficient and robust ML algorithms for wireless security that can operate under constrained power and computational resources. This is paramount for guaranteeing the integrity of wireless communications.

Undoubtedly, there is a pressing need to investigate the interactions between ML and wireless security, privacy, and robustness. To address this, our workshop aims to bring together members of the ML, privacy, security, wireless communications, and networking communities worldwide. It provides a platform to share the latest research findings in these emerging and critical areas, fostering the exchange of ideas and promoting research collaborations to advance the state-of-the-art.

Topics of Interest (but not limited to)

Adversarial ML Techniques

  • Adversarial examples
  • Adversarial reinforcement learning
  • Defense techniques
  • Generative adversarial learning
  • Poisoning attacks
  • Spoofing attacks
  • 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
  • Physical layer privacy

ML Applications

  • 5G/IoT/cloud security
  • Access control
  • Anonymity
  • Covert communications
  • Device identification
  • Digital twin security
  • Integrated sensing and communication (ISAC) security
  • Intrusion detection
  • Localization
  • Network virtualization
  • O-RAN security
  • RF fingerprinting
  • Security for mobile autonomous multi-agent platforms
  • Semantic and task-oriented communications
  • Smart jamming, spoofing, and mitigation

Strengthening ML Solutions

  • Authentication
  • Certified defense
  • Cognitive radio
  • Correcting for model or data drift
  • Data augmentation
  • Datasets
  • Efficient and edge deployable solutions
  • Embedded computing
  • Experiments and testbeds
  • Explainable ML for trusted security
  • Federated learning
  • Hardware solutions
  • Information discovery
  • Lifelong learning
  • Privacy-preserving learning
  • Secure learning
  • Uncertainty quantification

Workshop Chairs

Minhoe Kim
Korea University
Seoul, South Korea
Gihyuk Ko
Daejeon, South Korea
Yalin Sagduyu
Virginia Tech
Blacksburg, VA, USA
Yi Shi
Virginia Tech
Blacksburg, VA, USA

Steering Committee

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

Technical Program Committee

  • Eyuphan Bulut, Virginia Commonwealth University, USA
  • M. Cenk Gursoy, Syracuse University, USA
  • Rose Hu, Utah State University, USA
  • Jacek Kibilda, Virginia Tech, USA
  • Silvija Kokalj-Filipovic, Rowan University, USA
  • Zhuo Lu, University of South Florida, USA
  • Javier Parra-Arnau, Universitat Politècnica de Catalunya, Spain
  • Stjepan Picek, Radboud University, Netherlands
  • Danda B. Rawat, Howard University, USA
  • Heejun Roh, Inha University, Korea
  • Dola Saha, SUNY Albany, USA
  • Vijay Shah, George Mason University, USA
  • Lei Shi, Hefei University of Technology, China
  • Ayse Ünsal, EURECOM, France
  • Ning Wang, University of South Florida, USA
  • Kai Zeng, George Mason University, USA
  • Junqing Zhang, University of Liverpool, UK
  • Gyuhyeon Choi, KAIST, Korea

Submission Guidelines

Submission site:

Extended abstracts must be written in English and are not to exceed two pages. Only PDF files will be accepted for the review process. All abstracts must be thoroughly anonymized for double-blind reviewing.

Final 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.

Important Dates:

  • Extended Abstract Submission Deadline: March 10, 2024
  • Acceptance Notification: March 22, 2024
  • Camera-Ready Paper Submission: April 7, 2024
  • Workshop Event: May 30, 2024