ACM Workshop on Wireless Security and Machine Learning (WiseML 2023)

The ACM Workshop on Wireless Security and Machine Learning (WiseML 2023) will be held in conjunction with the ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec 2023). 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 to effectively learn from spectrum data, solve complex tasks for IoT, 5G and beyond, and secure the emerging communication systems against adversaries. Recent research has demonstrated the efficacy of adversarial ML (AML) techniques to negatively impact the performance of ML based wireless systems, which requires better understanding on the impact of AML on wireless technologies. On the other hand, the proliferation of wireless devices operating with diverse communication technologies in heterogeneous spectrum environments has made them susceptible targets to various attacks. Harnessing efficient, robust ML algorithms for wireless security that can operate under constrained power and computational resources, is of paramount importance for guaranteeing the integrity of wireless communications. Undoubtedly, an effort to investigate the interactions between ML and wireless security, privacy, and robustness, would be both timely and indispensable. The purpose of this workshop is to bring together members of the ML, privacy, security, wireless communications, and networking communities from around the world and offer them the opportunity to share the latest research findings in these emerging and critical areas, as well as to exchange ideas and foster research collaborations, in order to further 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
    • Information theoretic privacy
    • Membership inference attacks
    • Model inversion
    • Physical layer privacy
  • ML Applications
    • 5G/IoT security
    • Access control
    • Anonymity
    • Authentication
    • Cloud provenance
    • Covert communications
    • Device identification
    • Interference and jammer mitigation
    • Intrusion detection
    • Localization
    • Network slicing
    • Network virtualization
    • RF fingerprinting
    • Security for mobile autonomous multi-agent platforms
    • Semantic and task-oriented communications
    • Smart jamming and spoofing
  • Strengthening ML Solutions
    • 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

Deniz Gunduz
Imperial College London, UK

Mohammad Malekzadeh
Nokia Bell Labs Cambridge, UK

Melek Önen

Yalin Sagduyu
Virginia Tech, USA

Yi Shi
Virginia Tech, USA

Junqing Zhang
University of Liverpool, UK

Steering Committee

Wenjing Lou
Virginia Tech, USA

Sennur Ulukus
University of Maryland, USA

K.P. (Suba) Subbalakshmi
Stevens Institute of Technology, Hoboken, New Jersey, USA

Aylin Yener
Thee Ohio State University, USA

Technical Program Committee

  • Anastasia Borovykh, Imperial College London, UK
  • He Fang, Soochow University, China
  • M. Cenk Gursoy, Syracuse University, USA
  • Rose Hu, Utah State University, USA
  • Burak Kantarci, University of Ottawa, Canada
  • Jacek Kibilda, Virginia Tech, USA
  • Silvija Kokalj-Filipovic, Rowan University, USA
  • Marwan Krunz, University of Arizona, USA
  • Zhuo Lu, University of South Florida, USA
  • Javier Parra-Arnau, Universitat Politècnica de Catalunya (UPC), Spain
  • Danda B. Rawat, Howard University, USA
  • Dola Saha, SUNY Albany, USA
  • Sina Sajadmanesh, École Polytechnique Fédérale de Lausanne (EPFL), France
  • Vijay Shah, George Mason University, USA
  • Ayse Ünsal, EURECOM, France
  • Diana-Alexandra Vasile, Nokia Bell Labs Cambridge, UK
  • Kai Zeng, George Mason University, USA

Submission Guidelines

Submission site: 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 15, 2023
  • Acceptance Notification: April 3, 2023
  • Camera-Ready Paper Submission: April 17, 2023
  • Workshop Event: June 1, 2023