Embedded Files
In conjunction with IEEE Global Communications Conference (GLOBECOM) 2024, 8 -12 December 2024, Cape Town, South Africa
Submission Link
Submission Link
https://edas.info/N32729
Important Dates
Important Dates
Paper submission deadline: 5 August, 2024 20 August, 2024
Notification of acceptance: 1 September 2024 15 September 2024
Camera-ready papers: 1 October 2024 10 October 2024
Workshop date: 8 December, 2024
Workshop Scope
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
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
Organization Team
Steering Committee
Steering Committee
Prof Eduard A. Jorswieck
TU Braunschweig, Germany
Prof Shui Yu
University of Technology Sydney, Australia
General Chairs
General Chairs
Prof Burak Kantarci
University of Ottawa, Canada
Dr Yi Shi
Virginia Tech, US
TPC Chairs
TPC Chairs
Dr Junqing Zhang
University of Liverpool, UK
Dr Xuyu Wang
Florida International University, US
Dr Alessandro Brighente
University of Padova, Italy
Prof He Fang
Fujian Normal University, China
Dr Guanxiong Shen
Southeast University, China
Speakers
Speakers
Professor Stefano Tomasin
University of Padova, Italy
Title: Machine Learning Meets Statistical Testing for Wireless Security
Program
Program
Keynote
Keynote
14:00-15:00
Professor Professor Stefano Tomasin, University of Padova, Italy
Title: Machine Learning Meets Statistical Testing for Wireless Security
Technical Session I
Technical Session I
15:00 - 15:18
Deep SIMO Auto-Encoder and Radio Frequency Hardware Impairments Modeling for Physical Layer Security
Coffee Break
Coffee Break
15:30-16:00
Technical Session I (Continued)
Technical Session I (Continued)
16:00-16:36
Semi-Supervised Federated Learning Based Non-IID Jamming Recognition Against Poisoning Attacks
Sparse Autoencoder-Driven Key Generation for Enhanced IoT Security
Technical Session II
Technical Session II
16:37-17:31
A Framing of Eavesdropper Policy Manipulation Attacks on RL-enabled Wireless Systems
An Online Adaptive Approach to Detecting Zero-day Attacks in IoT and IIoT Systems
DR-LSTM Detection Scheme for B5G DoS Traffic
Page updated
Google Sites
Report abuse