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WiseML '22: Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning
ACM2022 Proceeding
  • General Chair:
  • Murtuza Jadliwala
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
WiSec '22: 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks San Antonio TX USA 19 May 2022
ISBN:
978-1-4503-9277-8
Published:
16 May 2022
Sponsors:
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Abstract

We are very pleased to welcome you to the 4th ACM Workshop on Wireless Security and Machine Learning (WiseML). ACM WiseML continues to be the premier venue that brings together members of the machine learning, privacy, security, wireless communications, and networking communities from around the world, and offers them the opportunity to share their latest research findings in these emerging and critical areas. It also offers a shared platform to exchange ideas and foster research collaborations, in order to further advance the state of the art. This year, WiseML will be an in-person event in San Antonio, Texas, USA, and is hosted by the Department of Computer Science and the National Security Collaboration Center at the University of Texas at San Antonio (UTSA). The program will be presented in a single track.

The technical program this year features 14 outstanding papers that cover a wide variety of security, privacy, and adversarial machine learning problems relating to wireless networks, communications, mobile networks, 5G/IoT systems, cloud systems, cyber physical systems, smartphones, cognitive radios, and emerging applications. Our call for papers attracted 23 qualified submissions from across the globe. These submissions have been carefully reviewed by six Technical Program Committee (TPC) co-chairs, as well as external experts from academia, industrial research labs, and government organizations. WiseML's exciting technical program is enriched by the keynote talk "Accelerating RF Autonomy for Uncertain Environments," by Dr. John Davies from DARPA, a distinguished leader in the field of machine learning, adaptive radio frequency systems, real-time signal processing, and configurable computing. Warm thanks to the keynote speaker for joining us.

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SESSION: Keynote Talk
keynote
Public Access
Accelerating RF Autonomy for Uncertain Environments

Under the Radiofrequency (RF) Machine Learning Systems (RFMLS) program, DARPA developed foundational technologies that enable autonomous RF systems to learn directly from data. By applying deep neural networks (DNNs) directly to digitized RF signals, ...

SESSION: Session 1: RF Analytics
research-article
Analysis of Augmentation Methods for RF Fingerprinting under Impaired Channels

Cyber-physical systems such as autonomous vehicle networks are considered to be critical infrastructures in various applications. However, their mission critical deployment makes them prone to cyber-attacks. Radio frequency (RF) fingerprinting is a ...

research-article
Open Access
Online Stream Sampling for Low-Memory On-Device Edge Training for WiFi Sensing

Deploying machine learning models on-board edge devices allows for low latency model inference and data privacy by keeping sensor data local to the computation rather than at a central server. However, typical TinyML systems train a single global model ...

research-article
Open Access
Deep Learning-based Localization in Limited Data Regimes

As demand for radio spectrum increases with the widespread use of wireless devices, effective spectrum allocation requires more flexibility in terms of time, space, and frequency. In order to protect users in next-generation wireless networks from ...

research-article
Public Access
Voice Fingerprinting for Indoor Localization with a Single Microphone Array and Deep Learning

With the fast development of the Internet of Things (IoT), smart speakers for voice assistance have become increasingly important in smart homes, which offers a new type of human-machine interaction interface. Voice localization with microphone arrays ...

research-article
Open Access
Deep Learning for Spectrum Awareness and Covert Communications via Unintended RF Emanations

We present a deep learning-based spectrum sensing and covert communication framework for unintended (side-channel) electromagnetic emanations. Electronic devices release unintentional RF emissions (without using any RF transmitter) depending on their ...

SESSION: Session 2: Spotlight Session
research-article
Public Access
Systems View to Designing RF Fingerprinting for Real-World Operations

Great progress has been made recently in radio frequency (RF) machine learning (ML), including RF fingerprinting. Much of this work to date, however, has been limited in scope to proof-of-concept demonstrations or narrowly defined and tested under ...

research-article
Open Access
Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers

Convolutional Neural Networks (CNNs) are one of the most studied family of deep learning models for signal classification, including modulation, technology, detection, and identification. In this work, we focus on technology classification based on raw ...

research-article
KNEW: Key Generation using NEural Networks from Wireless Channels

Secret keys can be generated from reciprocal channels to be used for shared secret key encryption. However, challenges arise in practical scenarios from non-reciprocal measurements of reciprocal channels due to changing channel conditions, hardware ...

research-article
Public Access
MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radio

Dynamic resource allocation plays a critical role in the next generation of intelligent wireless communication systems. Machine learning has been leveraged as a powerful tool to make strides in this domain. In most cases, the progress has been limited ...

SESSION: Session 3: ML Applications and Security
research-article
Open Access
Can You Hear It?: Backdoor Attacks via Ultrasonic Triggers

This work explores backdoor attacks for automatic speech recognition systems where we inject inaudible triggers. By doing so, we make the backdoor attack challenging to detect for legitimate users and, consequently, potentially more dangerous. We ...

research-article
Public Access
Undermining Deep Learning Based Channel Estimation via Adversarial Wireless Signal Fabrication

Channel estimation is a crucial step in wireless communications. The estimator identifies the wireless channel distortions during the signal propagation and this information is further used for data precoding and decoding. Recent studies have shown that ...

research-article
Public Access
A Machine Learning-Driven Analysis of Phantom E911 Calls

Phantom Enhanced 911 (E911) calls are automatically generated 2 second calls, are a serious concern on cellular networks, and consume critical resources. As networks become increasingly complex, detecting and troubleshooting the causes of phantom E911 ...

research-article
Open Access
Beam Pattern Fingerprinting with Missing Features for Spoofing Attack Detection in Millimeter-Wave Networks

As one of the key enabling technologies of 5G wireless communication, millimeter-Wave (mmWave) technology unlocks the ultra-wide bandwidth opportunity in supporting high-throughput (e.g., multi-Gbps) and ultra-low latency applications at much lower cost-...

research-article
Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control

Digital twin (DT) technologies have emerged as a solution for real-time data-driven modeling of cyber physical systems (CPS) using the vast amount of data available by Internet of Things (IoT) networks. In this position paper, we elucidate unique ...

Contributors
  • The University of Texas at San Antonio

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