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A Federated Learning Client Selection Method via Multi-Task Deep Reinforcement Learning
2024 IEEE International Conference on Unmanned Systems (ICUS)
Year: 2024 | Conference Paper |
Federated Learning (FL) is a privacy-preserving paradigm for training machine learning (ML) models, crucial for data privacy and security protection. It has garnered significant attention from both industry and academia. Typically, clients are selected randomly for training and model aggregation in FL scenarios. However, heterogeneity in data distribution and hardware among devices leads to proble...Show More
A Federated Learning Mechanism with Feature Drift for Feature Distribution Skew
2024 27th International Conference on Information Fusion (FUSION)
Year: 2024 | Conference Paper |
Federated learning is a nascent distributed machine learning paradigm that enables multiple clients to collaborate in training a model for a specific task under the coordination of a central server, all while safeguarding the privacy of the user’s local data. Nevertheless, the constraint that distributed datasets must remain within local nodes introduces data heterogeneity in federated learning tr...Show More
Intrusion Detection for Unmanned Aerial Vehicles Security: A Tiny Machine Learning Model
Unmanned aerial vehicles (UAVs) are vulnerable to network attacks. Designing an effective intrusion detection system (IDS) for UAVs is crucial. However, UAVs have limited computing resources and need to deal with massive amounts of network data, which further increases the difficulty of detection. Moreover, most existing IDSs have large parameters. In this study, we develop a tiny machine learning...Show More
Incremental Update Intrusion Detection for Industry 5.0 Security: A Graph Attention Network-Enabled Approach
Yixuan Wu;Laisen Nie;Xuanrui Xiong;Balqies Sadoun;Lin Yang;Zhaolong Ning
The Industry 5.0 (I5) incorporates numerous emerging technologies that enable the perception, management, interaction, and control of real physical objects. As cyber attacks are becoming more intelligent and accessible, I5 security issues are gaining increasing prominence. An intrusion detection system plays an important role in network security by detecting and identifying various security threat...Show More
Hypernetworks-Based Hierarchical Federated Learning on Hybrid Non-IID Datasets for Digital Twin in Industrial IoT
Digital twin technology deployed in the industrial Internet of Things (IoT) will be unavailable because of data heterogeneity and data islands. As a machine learning approach for distributed architectures, federated learning generates a global model for edge devices by only transferring model parameters, and it ensures that the private data of each device is not leaked. This is naturally suitable ...Show More
Digital Twin for Transportation Big Data: A Reinforcement Learning-Based Network Traffic Prediction Approach
Laisen Nie;Xiaojie Wang;Qinglin Zhao;Zhigang Shang;Li Feng;Guojun Li
Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system h...Show More
Rumors Suppression in Healthcare System: Opinion-Based Comprehensive Learning Particle Swarm Optimization
Qiang He;Wei Qiao;Ali Kashif Bashir;Yuliang Cai;Laisen Nie;Yasser D. Al-Otaibi;Keping Yu
The rumors in the healthcare system have the attributes of fast spread and severe social influence. Even worse, it may cause the collapse of medical services and the death of many patients. To prevent its serious impact on society, the target of rumor suppression for the healthcare system is to restrain the spread of rumors (negative opinions) and maximize the spread of antirumors (positive opinio...Show More
Intelligent Intrusion Detection for Internet of Things Security: A Deep Convolutional Generative Adversarial Network-Enabled Approach
IEEE Internet of Things Journal
Cited by: Papers (35)
With the rapid advance of Internet of Things (IoT), it is difficult for cloud-centric computing to meet the requirements of low latency and ease of use. As an open and distributed system, edge computing integrates computing, networking, storage, and applications. It provides intelligent services on the edge of an IoT. The edge network is composed of various wireless and wired networks, and the com...Show More
Network Traffic Prediction for Intelligent Transportation Systems: A Reinforcement Learning Approach
Jian Song;Hua Liu;Laisen Nie;Zhaolong Ning;Mohammad S. Obaidat;Balqies Sadoun
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Vehicular Ad-Hoc Networks (VANETs), as the cru-cial support of Intelligent Transportation Systems (ITS), have received a great attention in recent years. Network traffic prediction is useful for network management and security in VANETs, such as network planning and anomaly detection. Due to the movement of nodes, the traffic flow in VANETs consists of a great number of irregular fluctuations, whi...Show More
A Redactable Blockchain Framework for Secure Federated Learning in Industrial Internet of Things
Jiannan Wei;Qinchuan Zhu;Qianmu Li;Laisen Nie;Zhangyi Shen;Kim-Kwang Raymond Choo;Keping Yu
IEEE Internet of Things Journal
Cited by: Papers (42)
Industrial Internet of Things (IIoT) facilitate private data collecting via (a broad range of) sensors, and the analysis of such data can inform decision making at different levels. Federated learning (FL) can be used to analyze the collected data, in privacy-preserving manner by transmitting model updates instead of private data in IIoT networks. The FL framework is, however, vulnerable because m...Show More
A NOMA-Enabled Framework for Relay Deployment and Network Optimization in Double-Layer Airborne Access VANETs
Yixin He;Laisen Nie;Tan Guo;Kuljeet Kaur;Mohammad Mehedi Hassan;Keping Yu
A non-orthogonal multiple access (NOMA)-enabled double-layer airborne access vehicular ad hoc networks (DLAA-VANETs) architecture is designed in this paper, which consists of a high-altitude platform (HAP), multiple unmanned aerial vehicles (UAVs) and vehicles. For the designed DLAA-VANETs, we investigate the UAV deployment and network optimization problems. In particular, a UAV deployment scheme ...Show More
A Multitask Learning-Based Network Traffic Prediction Approach for SDN-Enabled Industrial Internet of Things
With the rapid advance of industrial Internet of Things (IIoT), to provide flexible access for various infrastructures and applications, software-defined networks (SDNs) have been involved in constructing current IIoT networks. To improve the quality of services of industrial applications, network traffic prediction has become an important research direction, which is beneficial for network manage...Show More
Distributed Orchestration of Service Function Chains for Edge Intelligence in the Industrial Internet of Things
Handi Chen;Shupeng Wang;Guojun Li;Laisen Nie;Xiaojie Wang;Zhaolong Ning
Network virtualization techniques are promising to overcome the obstacle of applying and expanding costly traditional networks in the industrial Internet of things (IIoT). Artificial intelligence (AI)-enhanced distributed resource management in edge networks has aroused researchers’ widespread attention. However, dynamically arrived service requests and limited edge resources complicate the servic...Show More
Delay-Sensitive Secure NOMA Transmission for Hierarchical HAP–LAP Medical-Care IoT Networks
Dawei Wang;Yixin He;Keping Yu;Gautam Srivastava;Laisen Nie;Ruonan Zhang
Medical-care Internet of Things enables rapid medical assistance by providing comprehensive and clear healthy information. However, due to the limited infrastructure, it is difficult to quickly and securely transmit medical-care information in poverty-stricken or disaster-stricken areas. To tackle the above situation, in this article, we propose a delay-sensitive secure nonorthogonal multiple acce...Show More
Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach
Laisen Nie;Yixuan Wu;Xiaojie Wang;Lei Guo;Guoyin Wang;Xinbo Gao;Shengtao Li
The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices,...Show More
Imitation-Learning-Enabled Vehicular Edge Computing: Toward Online Task Scheduling
Laisen Nie;Xiaojie Wang;Wentao Sun;Yongkang Li;Shengtao Li;Peng Zhang
Vehicular edge computing (VEC) is a promising paradigm to enable information sharing and acquisition among vehicles. Since information freshness has significantly influence on task scheduling, an emerging metric, named the age of information (AoI), has been utilized to evaluate it. Recent research generally focuses on AoI minimization but pays little attention to information personality. However, ...Show More
Intrusion Detection in Green Internet of Things: A Deep Deterministic Policy Gradient-Based Algorithm
Laisen Nie;Wentao Sun;Shupeng Wang;Zhaolong Ning;Joel J. P. C. Rodrigues;Yixuan Wu;Shengtao Li
Internet of Things (IoT) is an expanded application of Internet, which are used to provide various services for users. Up to now, IoTs have received more attention, because it can provide ubiquitous connectivity for various devices. With the development of IoTs, its security has become a main issue. Attackers use various techniques to implement cyber attacks for the IoT, which threats the users' p...Show More
Network Traffic Prediction in Industrial Internet of Things Backbone Networks: A Multitask Learning Mechanism
Laisen Nie;Xiaojie Wang;Shupeng Wang;Zhaolong Ning;Mohammad S. Obaidat;Balqies Sadoun;Shengtao Li
Industrial Internet of Things (IIoT), as a common industrial application of Internet of Things, has been widely deployed in recent years. End-to-end network traffic is an essential information for many network security and management functions. This article investigates the issues of IIoT-oriented backbone network traffic prediction. Predicting the traffic of IIoT backbone networks is intractable ...Show More
Blockchain Based IIoT Data Sharing Framework for SDN-Enabled Pervasive Edge Computing
Ying Gao;Yijian Chen;Xiping Hu;Hongliang Lin;Yangliang Liu;Laisen Nie
Pervasive edge computing (PEC) is an emerging paradigm for the industrial Internet of Things (IIoT), and software-defined networks (SDN) offer lower latency services, and massive intelligent devices connectivity for the IIoT. However, the PEC has some issues with data security, and privacy while PEC devices sharing data among edges. What's more, the centralized SDN suffers from single point of att...Show More
A Reinforcement Learning-Based Network Traffic Prediction Mechanism in Intelligent Internet of Things
Laisen Nie;Zhaolong Ning;Mohammad S. Obaidat;Balqies Sadoun;Huizhi Wang;Shengtao Li;Lei Guo;Guoyin Wang
Intelligent Internet of Things (IIoT) is comprised of various wireless and wired networks for industrial applications, which makes it complex and heterogeneous.The openness of IIoT has led to the intractable problems of network security and management. Many network security and management functions rely on network traffic prediction techniques, such as anomaly detection and predictive network plan...Show More
Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method
Laisen Nie;Zhaolong Ning;Xiaojie Wang;Xiping Hu;Jun Cheng;Yongkang Li
As an industrial application of Internet of Things (IoT), Internet of Vehicles (IoV) is one of the most crucial techniques for Intelligent Transportation System (ITS), which is a basic element of smart cities. The primary issue for the deployment of ITS based on IoV is the security for both users and infrastructures. The Intrusion Detection System (IDS) is important for IoV users to keep them away...Show More
The end-to-end network traffic information is the basis of network management for a large-scale intelligent transportation systems-oriented backbone network. To obtain exact network traffic data, a prevalent idea is to deploy NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, but also affects the network load. Motivated by this issue, ...Show More
Study on the range equation modeling of MEO SAR-GMTI systems
2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)
Year: 2019 | Conference Paper |
Due to the advantages of large coverage, short revisit time, and long synthetic aperture time, medium-earth-orbit (MEO) synthetic aperture radar (SAR)-ground moving target indication (GMTI) system is an attractive tool for space-to-ground surveillance and reconnaissance. However, the nonlinear motion of MEO SAR and the significant earth rotation dramatically complicate the relative motion between ...Show More
Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs
Laisen Nie;Huizhi Wang;Shimin Gong;Zhaolong Ning;Mohammad S. Obaidat;Kuei-Fang Hsiao
2019 IEEE Global Communications Conference (GLOBECOM)
Year: 2019 | Conference Paper |
Cited by: Papers (9)
Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection f...Show More
Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale IP Backbone Networks
Huizhi Wang;Laisen Nie;Zhaolong Ning;Mohammad S. Obaidat;Runze Shang
2019 IEEE Global Communications Conference (GLOBECOM)
Year: 2019 | Conference Paper |
The end-to-end network traffic information is the basis of network management in large-scale IP backbone networks. To obtain exact network traffic data, a prevalent idea is to employ NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, it also affects the network load. Motivated by this issue, we propose an optimized traffic measurement ...Show More
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