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Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) communication networks through the integration of multi-task learning.
Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution
This paper studies the poisoning attack and defense interactions in a federated learning (FL) system, specifically in the context of wireless signal classification using deep learning for next-generation (NextG) communications.
Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning
Results presented in this paper quantify the level of transferability of adversarial attacks on different LoRa signal classification tasks as a major vulnerability and highlight the need to make IoT applications robust to adversarial attacks.
Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access
Recognizing the computational constraints and trust issues associated with on-device computation, we propose a collaborative system wherein the edge device communicates selectively processed information to a trusted receiver acting as a fusion center, where a decision is made to identify whether a potential transmitter is present, or not.
Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidance
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV non-cooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a challenging task.
Joint Sensing and Semantic Communications with Multi-Task Deep Learning
The transmitter employs a deep neural network, namely an encoder, for joint operations of source coding, channel coding, and modulation, while the receiver utilizes another deep neural network, namely a decoder, for joint operations of demodulation, channel decoding, and source decoding to reconstruct the data samples.
Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning
A multi-task deep learning approach that involves training a common encoder at the transmitter and individual decoders at the receivers is presented for joint optimization of completing multiple tasks and communicating with multiple receivers.
Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks
We show that the DFL performance can be significantly reduced by jamming attacks launched in a wireless network and characterize the attack surface as a vulnerability study before the safe deployment of DFL over wireless networks.
Age of Information in Deep Learning-Driven Task-Oriented Communications
This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter.
Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing
In this paper, we present a generative adversarial network (GAN) approach to generate synthetic sensing results to augment the training data for the deep learning classifier so that the sensing time can be reduced (and thus the transmission time can be increased) while keeping high accuracy of the classifier.
Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning
The performance in Nash equilibrium is compared to the fixed attack and defense cases, and the resilience of NextG signal classification against attacks is quantified.
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks
The backdoor attack can effectively change the semantic information transferred for the poisoned input samples to a target meaning.
Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks
This tradeoff leads to a non-cooperative game where each client aims to individually maximize its utility as the difference between the global model accuracy and the cost of FL participation.
Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks
By augmenting the reconstruction loss with a semantic loss, the two deep neural networks (DNNs) of this encoder-decoder pair are interactively trained with the DNN of the semantic task classifier.
Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects
In this paper, wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label.
Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems
By approximating the Q-values with a DQN, DRL is implemented for the embedded platform of each node combining an ARM processor and a WiFi transceiver for 802. 11n.
Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning
Self-supervised learning (SSL) enables the learning of useful representations from Radio Frequency (RF) signals themselves even when only limited training data samples with labels are available.
Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks
In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a DNN for signal identification.
Machine Learning in NextG Networks via Generative Adversarial Networks
GANs have the following advantages.
Jamming Attacks on Federated Learning in Wireless Networks
Federated learning (FL) offers a decentralized learning environment so that a group of clients can collaborate to train a global model at the server, while keeping their training data confidential.
End-to-End Autoencoder Communications with Optimized Interference Suppression
An end-to-end communications system based on Orthogonal Frequency Division Multiplexing (OFDM) is modeled as an autoencoder (AE) for which the transmitter (coding and modulation) and receiver (demodulation and decoding) are represented as deep neural networks (DNNs) of the encoder and decoder, respectively.
Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces
Results from different network topologies show that adversarial perturbation and RIS interaction vector can be jointly designed to effectively increase the signal detection accuracy at the receiver while reducing the detection accuracy at the eavesdropper to enable covert communications.
Autoencoder-based Communications with Reconfigurable Intelligent Surfaces
This paper presents a novel approach for the joint design of a reconfigurable intelligent surface (RIS) and a transmitter-receiver pair that are trained together as a set of deep neural networks (DNNs) to optimize the end-to-end communication performance at the receiver.
Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications
The DNN that corresponds to a regression model is trained with channel gains as the input and returns transmit powers as the output.
Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning
An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier.
Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks.
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond
Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications.
Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction
To integrate RISs into THz drone communications, we propose a novel deep learning solution based on a recurrent neural network, namely the Gated Recurrent Unit (GRU), that proactively predicts the serving base station/RIS and the serving beam for each drone based on the prior observations of drone location/beam trajectories.
Information Theory Networking and Internet Architecture Information Theory
Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing
We show that the portion of the reward achieved by real requests may be much less than the reward that would be achieved when there was no attack.
How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning
In this paper, reinforcement learning (RL) for network slicing is considered in NextG radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total reward of accepted requests over time.
Networking and Internet Architecture
Adversarial Machine Learning for 5G Communications Security
For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station.
Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks
In LoS conditions, the selection of the beams is consequential and improves the accuracy by up to 70%.
Adversarial Machine Learning in Wireless Communications using RF Data: A Review
In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented.
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers
The transmitter is equipped with a deep neural network (DNN) classifier for detecting the ongoing transmissions from the background emitter and transmits a signal if the spectrum is idle.
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing.
Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers
First, we show that multiple independent adversaries, each with a single antenna cannot improve the attack performance compared to a single adversary with multiple antennas using the same total power.
Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers
As machine learning (ML) algorithms are used to process wireless signals to make decisions such as PHY-layer authentication, the training data characteristics (e. g., device-level information) and the environment conditions (e. g., channel information) under which the data is collected may leak to the ML model.
Adversarial Machine Learning based Partial-model Attack in IoT
In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices.
Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks
This paper presents DeepIA, a deep learning solution for faster and more accurate initial access (IA) in 5G millimeter wave (mmWave) networks when compared to conventional IA.
How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy
We consider the problem of hiding wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect whether any transmission of interest is present or not.
Deep Learning for Wireless Communications
Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom.
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
There is a transmitter that transmits signals with different modulation types.
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels
In the meantime, the adversary makes over-the-air transmissions that are received as superimposed with the transmitter's signals to fool the classifier at the receiver into making errors.
When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions
Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium.
Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks
A transmitter applies deep learning on its spectrum sensing results to predict idle time slots for data transmission.
DeepWiFi: Cognitive WiFi with Deep Learning
We present the DeepWiFi protocol, which hardens the baseline WiFi (IEEE 802. 11ac) with deep learning and sustains high throughput by mitigating out-of-network interference.
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning
A deep learning classifier is considered to classify wireless signals using raw (I/Q) samples as features and modulation types as labels.
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks.
Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification
We designed and implemented a deep learning based RF signal classifier on the Field Programmable Gate Array (FPGA) of an embedded software-defined radio platform, DeepRadio, that classifies the signals received through the RF front end to different modulation types in real time and with low power.
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments
Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network.
IoT Network Security from the Perspective of Adversarial Deep Learning
While there is a surge of interest to understand the security issues of machine learning, their implications have not been understood yet for wireless applications such as those in IoT systems that are susceptible to various attacks due the open and broadcast nature of wireless communications.
When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing
In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center.
Generative Adversarial Network for Wireless Signal Spoofing
Building upon deep learning techniques, this paper introduces a spoofing attack by an adversary pair of a transmitter and a receiver that assume the generator and discriminator roles in the GAN and play a minimax game to generate the best spoofing signals that aim to fool the best trained defense mechanism.
Spectrum Data Poisoning with Adversarial Deep Learning
We consider the case that a cognitive transmitter senses the spectrum and transmits on idle channels determined by a machine learning algorithm.
Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data
The exploratory attack with limited training data is shown to fail to reliably infer the target classifier of a real text classifier API that is available online to the public.
BIG-bench Machine Learning
Generative Adversarial Network
+1
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls
To mitigate the impact of limited training data, we develop an active learning approach that first builds a classifier based on a small number of API calls and uses this classifier to select samples to further collect their labels.
Deep Learning for Launching and Mitigating Wireless Jamming Attacks
An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented.
Generative Adversarial Learning for Spectrum Sensing
A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio.
