Search Results for author:
Found 35 papers, 2 papers with code
Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection
The output of TAE called the \textit{reconstruction representation} is input to downstream models to detect cyberattacks.
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems
The MIAE model is trained in an unsupervised learning mode to transform the heterogeneous inputs into lower-dimensional representation, which helps classifiers distinguish between normal behaviour and different types of attacks.
CNN-FL for Biotechnology Industry Empowered by Internet-of-BioNano Things and Digital Twins
In this work, we propose a novel framework that integrates the Internet of Bio-Nano Things (IoBNT) with advanced machine learning techniques, specifically convolutional neural networks (CNN) and federated learning (FL), to effectively tackle the identified challenges.
Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study
Generative Artificial Intelligence (GAI) has recently emerged as a promising solution to address critical challenges of blockchain technology, including scalability, security, privacy, and interoperability.
Generative AI for Physical Layer Communications: A Survey
The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity.
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems
Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks.
Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT Sensing
In particular, We first formulate an optimization problem that harnesses the sampling process to concurrently reduce overfitting while maximizing accuracy.
Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation
This work for the first time investigates a resource allocation problem in collaborative sensing-assisted sustainable FL (S2FL) networks with the goal of minimizing the total completion time.
Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning
This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before.
Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework
To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN).
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach
FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset.
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence
A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, local epoch number.
Toward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach
Our proposed framework involves a mixed decision-making and classification problem in which the base station has to allocate its computing and radio resources to the users and classify the brain signals of users in an efficient manner.
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing
This theoretical bound is decomposed into two components, including the variance of the global gradient and the quadratic bias that can be minimized by optimizing the communication resources, and quantization/noise parameters.
Label driven Knowledge Distillation for Federated Learning with non-IID Data
Moreover, leveraging the advantages of hierarchical network design, we propose a new label-driven knowledge distillation (LKD) technique at the global server to address the second problem.
Frequency Hopping Joint Radar-Communications with Hybrid Sub-pulse Frequency and Duration
Frequency-hopping (FH) joint radar-communications (JRC) can offer excellent security for integrated sensing and communication systems.
HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks
Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing.
Collaborative Learning for Cyberattack Detection in Blockchain Networks
The main idea of the proposed learning model is to enable blockchain nodes to actively collect data, share the knowledge learned from its data, and then exchange the knowledge with other blockchain nodes in the network.
Multiple Correlated Jammers Nullification using LSTM-based Deep Dueling Neural Network
Specifically, recent studies reveal that by deliberately varying the correlations among jamming signals, attackers can effectively vary the jamming channels and thus their nullspace, even when the physical channels remain unchanged.
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks
Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks.
Managing Interference and Leveraging Secondary Reflections Amongst Multiple IRSs
This work considers the uplink of multiple users that are grouped and supported by multiple IRSs to a multi-antenna base station.
Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services
In this work, we propose a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, taking into account limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP.
Joint Coding and Scheduling Optimization for Distributed Learning over Wireless Edge Networks
The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.
Transfer Learning for Future Wireless Networks: A Comprehensive Survey
Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
FedChain: Secure Proof-of-Stake-based Framework for Federated-blockchain Systems
Furthermore, the game model can enhance the security and performance of FedChain.
Computer Science and Game Theory Cryptography and Security
Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance
We investigate the performance of multi-user multiple-antenna downlink systems in which a BS serves multiple users via a shared wireless medium.
Information Theory Information Theory
Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles
Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure.
Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications
Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks.
DeepFake: Deep Dueling-based Deception Strategy to Defeat Reactive Jammers
In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks.
Face Swapping
Networking and Internet Architecture
Information Theory
Signal Processing
Information Theory
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach
To that end, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly obtain the optimal policy by simultaneously learning from various vehicles.
Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks
In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network.
Networking and Internet Architecture Signal Processing
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks
Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24. 63% and decrease communication overhead by 83. 4% compared with other baseline machine learning algorithms.
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications
Bringing together the latest advances in neural network architectures and ambient backscattering communications, this work allows wireless nodes to effectively "face" the jammer by first learning its jamming strategy, then adapting the rate or transmitting information right on the jamming signal.
Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks
This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants.
Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning
To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy.
