Search Results for author: Yalin E. Sagduyu

Found 58 papers, 0 papers with code

Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing

no code implementations3 Jan 2024 Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

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.

Federated Learning Multi-Task Learning +1

Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution

no code implementations28 Dec 2023 Yalin E. Sagduyu, Tugba Erpek, Yi Shi

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.

Federated Learning

Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning

no code implementations27 Dec 2023 Yalin E. Sagduyu, Tugba Erpek

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.

Classification Density Estimation

Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access

no code implementations21 Dec 2023 Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

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.

Decoder Image Classification

Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidance

no code implementations11 Dec 2023 Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek, Yalin E. Sagduyu

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.

Collision Avoidance Decision Making

Joint Sensing and Semantic Communications with Multi-Task Deep Learning

no code implementations8 Nov 2023 Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

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.

Decoder Multi-Task Learning

Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning

no code implementations14 Aug 2023 Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

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.

Image Classification Multi-Task Learning

Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks

no code implementations12 Jan 2023 Yi Shi, Yalin E. Sagduyu, Tugba Erpek

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.

Federated Learning

Age of Information in Deep Learning-Driven Task-Oriented Communications

no code implementations11 Jan 2023 Yalin E. Sagduyu, Sennur Ulukus, Aylin Yener

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.

Decoder

Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing

no code implementations27 Dec 2022 Yi Shi, Yalin E. Sagduyu

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.

Generative Adversarial Network

Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning

no code implementations22 Dec 2022 Yalin E. Sagduyu

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

no code implementations21 Dec 2022 Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus, Aylin Yener

The backdoor attack can effectively change the semantic information transferred for the poisoned input samples to a target meaning.

Backdoor Attack Decoder

Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks

no code implementations21 Dec 2022 Yalin E. Sagduyu

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.

Federated Learning

Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks

no code implementations20 Dec 2022 Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus, Aylin Yener

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.

Decoder

Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects

no code implementations19 Dec 2022 Yalin E. Sagduyu, Sennur Ulukus, Aylin Yener

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.

Decoder

Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems

no code implementations2 Nov 2022 Ziad El Jamous, Kemal Davaslioglu, Yalin E. Sagduyu

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.

Q-Learning reinforcement-learning +1

Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning

no code implementations7 Jul 2022 Kemal Davaslioglu, Serdar Boztas, Mehmet Can Ertem, Yalin E. Sagduyu, Ender Ayanoglu

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.

Automatic Modulation Recognition Representation Learning +2

Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks

no code implementations6 Apr 2022 Yi Shi, Yalin E. Sagduyu, Tugba Erpek

In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a DNN for signal identification.

Anomaly Detection Federated Learning

Jamming Attacks on Federated Learning in Wireless Networks

no code implementations13 Jan 2022 Yi Shi, Yalin E. Sagduyu

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.

Federated Learning

End-to-End Autoencoder Communications with Optimized Interference Suppression

no code implementations29 Dec 2021 Kemal Davaslioglu, Tugba Erpek, Yalin E. Sagduyu

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.

Decoder Generative Adversarial Network +1

Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces

no code implementations21 Dec 2021 Brian Kim, Tugba Erpek, Yalin E. Sagduyu, Sennur Ulukus

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.

BIG-bench Machine Learning

Autoencoder-based Communications with Reconfigurable Intelligent Surfaces

no code implementations8 Dec 2021 Tugba Erpek, Yalin E. Sagduyu, Ahmed Alkhateeb, Aylin Yener

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.

Decoder

Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications

no code implementations16 Sep 2021 Brian Kim, Yi Shi, Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus

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

no code implementations22 Jul 2021 Yi Shi, Yalin E. Sagduyu

An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier.

Inference Attack Membership Inference Attack

Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond

no code implementations25 Mar 2021 Brian Kim, Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus

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.

Adversarial Attack

Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction

no code implementations22 Feb 2021 Nof Abuzainab, Muhammad Alrabeiah, Ahmed Alkhateeb, Yalin E. Sagduyu

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

no code implementations21 Jan 2021 Yi Shi, Yalin E. Sagduyu

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.

BIG-bench Machine Learning Reinforcement Learning (RL)

How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning

no code implementations14 Jan 2021 Yi Shi, Yalin E. Sagduyu, Tugba Erpek, M. Cenk Gursoy

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

no code implementations7 Jan 2021 Yalin E. Sagduyu, Tugba Erpek, Yi Shi

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.

BIG-bench Machine Learning Generative Adversarial Network

Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks

no code implementations6 Jan 2021 Tarun S. Cousik, Vijay K. Shah, Tugba Erpek, Yalin E. Sagduyu, Jeffrey H. Reed

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

no code implementations28 Dec 2020 Damilola Adesina, Chung-Chu Hsieh, Yalin E. Sagduyu, Lijun Qian

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.

BIG-bench Machine Learning

Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers

no code implementations3 Dec 2020 Brian Kim, Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu, Sennur Ulukus

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.

Adversarial Attack

Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing

no code implementations14 Sep 2020 Yi Shi, Yalin E. Sagduyu, Tugba Erpek

The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing.

Q-Learning reinforcement-learning +1

Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers

no code implementations31 Jul 2020 Brian Kim, Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu, Sennur Ulukus

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.

Diversity

Adversarial Machine Learning based Partial-model Attack in IoT

no code implementations25 Jun 2020 Zhengping Luo, Shangqing Zhao, Zhuo Lu, Yalin E. Sagduyu, Jie Xu

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.

BIG-bench Machine Learning Decision Making

Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers

no code implementations25 Jun 2020 Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu

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.

Inference Attack Membership Inference Attack

Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks

no code implementations22 Jun 2020 Tarun S. Cousik, Vijay K. Shah, Jeffrey H. Reed, Tugba Erpek, Yalin E. Sagduyu

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

no code implementations15 May 2020 Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus

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.

BIG-bench Machine Learning

Deep Learning for Wireless Communications

no code implementations12 May 2020 Tugba Erpek, Timothy J. O'Shea, Yalin E. Sagduyu, Yi Shi, T. Charles Clancy

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.

Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels

no code implementations5 Feb 2020 Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus

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.

Adversarial Attack

When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions

no code implementations24 Jan 2020 Yalin E. Sagduyu, Yi Shi, Tugba Erpek, William Headley, Bryse Flowers, George Stantchev, Zhuo Lu

Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium.

BIG-bench Machine Learning

Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks

no code implementations1 Nov 2019 Yalin E. Sagduyu, Yi Shi, Tugba Erpek

A transmitter applies deep learning on its spectrum sensing results to predict idle time slots for data transmission.

DeepWiFi: Cognitive WiFi with Deep Learning

no code implementations29 Oct 2019 Kemal Davaslioglu, Sohraab Soltani, Tugba Erpek, Yalin E. Sagduyu

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

no code implementations23 Oct 2019 Kemal Davaslioglu, Yalin E. Sagduyu

A deep learning classifier is considered to classify wireless signals using raw (I/Q) samples as features and modulation types as labels.

BIG-bench Machine Learning Classification +3

QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning

no code implementations13 Oct 2019 Nof Abuzainab, Tugba Erpek, Kemal Davaslioglu, Yalin E. Sagduyu, Yi Shi, Sharon J. Mackey, Mitesh Patel, Frank Panettieri, Muhammad A. Qureshi, Volkan Isler, Aylin Yener

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.

reinforcement-learning Reinforcement Learning (RL)

Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification

no code implementations13 Oct 2019 Sohraab Soltani, Yalin E. Sagduyu, Raqibul Hasan, Kemal Davaslioglu, Hongmei Deng, Tugba Erpek

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.

General Classification

Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments

no code implementations25 Sep 2019 Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu, William C. Headley, Michael Fowler, Gilbert Green

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.

blind source separation Classification +5

IoT Network Security from the Perspective of Adversarial Deep Learning

no code implementations31 May 2019 Yalin E. Sagduyu, Yi Shi, Tugba Erpek

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.

BIG-bench Machine Learning Information Retrieval +2

When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing

no code implementations4 May 2019 Zhengping Luo, Shangqing Zhao, Zhuo Lu, Jie Xu, Yalin E. Sagduyu

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.

BIG-bench Machine Learning

Generative Adversarial Network for Wireless Signal Spoofing

no code implementations3 May 2019 Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu

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.

Generative Adversarial Network

Spectrum Data Poisoning with Adversarial Deep Learning

no code implementations26 Jan 2019 Yi Shi, Tugba Erpek, Yalin E. Sagduyu, Jason H. Li

We consider the case that a cognitive transmitter senses the spectrum and transmits on idle channels determined by a machine learning algorithm.

BIG-bench Machine Learning Data Poisoning

Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data

no code implementations25 Jan 2019 Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu, Jason H. Li

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

no code implementations5 Nov 2018 Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu, Jason H. Li

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.

Active Learning BIG-bench Machine Learning +1

Deep Learning for Launching and Mitigating Wireless Jamming Attacks

no code implementations3 Jul 2018 Tugba Erpek, Yalin E. Sagduyu, Yi Shi

An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented.

Generative Adversarial Network

Generative Adversarial Learning for Spectrum Sensing

no code implementations2 Apr 2018 Kemal Davaslioglu, Yalin E. Sagduyu

A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio.

BIG-bench Machine Learning Data Augmentation +2

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