Workshop on Machine Learning and Deep Learning for Wireless Security
In conjunction with IEEE International Conference on Communications (ICC) 2024, 9 -13 June 2024, Denver, CO, USA
Submission Link
Important Dates
Paper submission deadline: 2 Feb, 2024
Notification of acceptance: 6 March 2024
Camera-ready papers: 15 March 2024
Workshop date: 9 June, 2024
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 Interest
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
Prof Shui Yu
University of Technology Sydney, Australia
Prof Burak Kantarci
University of Ottawa, Canada
Dr Yi Shi
Virginia Tech, USA
Technical Program Committee Chairs (TPC)
Dr Junqing Zhang
University of Liverpool, UK
Dr Xuyu Wang
Florida International University, USA
Dr Alessandro Brighente
University of Padova, Italy
Dr Guanxiong Shen
Southeast University, China
Speakers
Prof Kaushik Chowdhury
Northeastern University, USA
Prof Michele Nogueira
Federal University of Minas Gerais, Brazil
Title: RF Fingerprinting: Challenges and Experiences in Real-world Applications
Title: Empowering DDoS Attack Prediction through Machine Learning and Deep Learning
Schedule
Date: Sunday, June 9
8:30 - 08:33
Opening Session
8:33 - 9:33
Keynote Session 1
RF Fingerprinting: Challenges and Experiences in Real-world Applications
Kaushik Chowdhury (Northeastern University, USA)
9:33 – 10:15
Session 1: Deep Learning for RF Fingerprinting and Automatic Modulation Classification
Classification of RF Transmitters in the Presence of Multipath Effects using CNN-LSTM
Pradnya Patil, Zhuangkun Wei, Ivan Petrunin and Weisi Guo (Cranfield University, United Kingdom (Great Britain))
AI Generated Wireless Data for Enhanced Satellite Device Fingerprinting
Ningning Wang and Tianya Zhao (Florida International University, USA); Shiwen Mao (Auburn University, USA); Xuyu Wang (Florida International University, USA)
10:15 – 10:45
Coffee Break
10:45 – 11:27
Session 1: Deep Learning for RF Fingerprinting and Automatic Modulation Classification (Continued)
Spectrum Enhancement Based Modulation Recognition with Dual-Cue Attention Fusion and Extraction
Jiaqi Gao, Jie Li, Siqin Ning and Qihui Wu (Nanjing University of Aeronautics and Astronautics, China)
Temperature Sensitivity of RFML Algorithms
Brennan E Olds (Virginia Polytechnic Institute and State University & Hume Center for National Security and Technology, USA); Alan J Michaels (Virginia Tech & Hume Center for National Security and Technology, USA)
11:27 – 12:30
Session 2: Deep Learning for Attack Prediction and Detection
Multifaceted DDoS Attack Prediction by Multivariate Time Series and Ordinal Patterns
Ligia F. Borges (Federal University of Minas Gerais - UFMG, Brazil); Anderson Bergamini de Neira (Federal University of Parana, Brazil); Lucas Albano and Michele Nogueira (Federal University of Minas Gerais, Brazil)
Optimized Ensemble Model with Genetic Algorithm for DDoS Attack Detection in IoT Networks
Makhduma Farukali Saiyed and Irfan S. Al-Anbagi (University of Regina, Canada)
AE-BiLSTM: Multivariate Time-Series EMI Anomaly Detection in 5G-R High-Speed Rail Wireless Communications
Yejing Fan, Li Zhang and Kang Li (University of Leeds, United Kingdom (Great Britain))
12:30 – 13:30
Lunch Break
13:30 – 14:30
Keynote Session 2
Empowering DDoS Attack Prediction through Machine Learning and Deep Learning
Michele Nogueira (Federal University of Minas Gerais, Brazil)
14:30 – 15:15
Session 2: Deep Learning for Attack Prediction and Detection (Continued)
Detecting 5G Signal Jammers Using Spectrograms with Supervised and Unsupervised Learning
Matteo Varotto (Hochschule Darmstadt, Germany); Stefan Valentin (Darmstadt University of Applied Sciences, Germany); Stefano Tomasin (University of Padova, Italy)
An Intelligent Digital Twin Model for Attack Detection in Zero-Touch 6G networks
Burcu Bolat-Akça (National Defence University, Turkey); Elif Bozkaya (National Defence University Turkish Naval Academy, Turkey); Berk Canberk and Bill Buchanan (Edinburgh Napier University, United Kingdom (Great Britain)); Stefan Schmid (TU Berlin, Germany)
15:15 – 15:45
Coffee Break
15:45 – 17:30
Session 3: Deep Learning for Secure Communication and Privacy
Knowledge Distillation-Based Robust UAV Swarm Communication Under Malicious Attacks
Qirui Wu and Yirun Zhang (King's College London, United Kingdom (Great Britain)); Zhaohui Yang (Zhejiang University, China); Mohammad Shikh-Bahaei (King's College London, United Kingdom (Great Britain))
Learning Asymmetric Cross Layer Encryption for Wireless Communication
Hesham Mohammed (University at Albany, SUNY, USA)
Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution Approach
George Stamatelis (National and Kapodistrian University of Athens, Greece); Angelos-Nikolaos Kanatas (National Technical University of Athens, Greece); Ioannis Asprogerakas (National and Technical University of Athens, Greece); George C. Alexandropoulos (University of Athens, Greece)
Detection of Unknown Signals at Low SNRs
Nathaniel W Rowe (University at Albany, USA); Dola Saha (University at Albany, SUNY, USA)
UFed-GAN: Secure Federated Learning over Wireless Sensor Networks with Unlabeled Data
Achintha Wijesinghe (University of California, Davis & University of Moratuwa, USA); Songyang Zhang (University of Louisiana at Lafayette, USA); Siyu Qi (University of California, USA); Zhi Ding (University of California at Davis, USA)