Last updated: 2024-05-06 03:01 UTC
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Number of pages: 113
Author(s) | Title | Year | Issue | Keywords | ||
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Suraj Kumar, Soumi Chattopadhyay, Chandranath Adak | TPMCF: Temporal QoS Prediction Using Multi-Source Collaborative Features | 2024 | Early Access | Quality of service Feature extraction Transformers Collaboration Data models Convolution Tensors Temporal QoS Prediction Graph Convolutional Matrix Factorization Predictive Transformer Encoder | The e-commerce industry has seen significant growth in recent years due to the introduction of new web service APIs. Quality-of-Service (QoS) parameters, which are fundamental for assessing service performance, have become crucial in evaluating services in the competitive market. Since QoS parameters can vary among users and change over time, accurate QoS predictions have become essential for users when selecting the most suitable services. Existing methods for predicting temporal QoS have hardly achieved the desired accuracy, beset by challenges like data sparsity, the presence of anomalies, and the inability to capture intricate temporal user-service interactions. Although some recent approaches, particularly those founded on recurrent neural network-based sequential architectures, endeavor to model temporal relationships in QoS data, they grapple with performance degradation due to the omission of other pivotal features, such as collaborative relationships and spatial characteristics of users and services. Furthermore, the uniform attention among features across all time-steps can thwart progress in predictive accuracy. This paper addresses these challenges and proffers a scalable strategy for temporal QoS prediction using multi-source collaborative features that not only furnishes heightened responsiveness but also engenders enhanced prediction accuracy. The method amalgamates collaborative features stemming from both users and services, capitalizing on the user-service relationship. Additionally, it integrates spatio-temporal auto-extracted features through the orchestration of graph convolution and a specialized variant of the transformer encoder equipped with multi-head self-attention. The proposed approach has been validated on the WSDREAM-2 benchmark datasets, and the results of these extensive experiments demonstrate that our framework surpasses major state-of-the-art methods in terms of predictive accuracy, all the while upholding robust scalability and reasonable responsiveness. | 10.1109/TNSM.2024.3395428 |
Rachid El-Azouzi, Francesco De Pellegrini, Afaf Arfaoui, Cedric Richier, Jeremie Leguay, Quang-Trung Luu, Youcef Magnouche, Sebastien Martin | Semi-Distributed Coflow Scheduling in Datacenters | 2024 | Early Access | Resource management Scheduling Switches Processor scheduling Task analysis Fabrics Standards Coflow scheduling σ-order distributed rate allocation pricing task scheduling resource allocation | With the advent of big data applications, coflow scheduling has become a cornerstone for the engineering of traffic in datacenters. Minimizing the average weighted Coflow Completion Times (CCT) is a crucial step to minimize the execution time of jobs running in distributed computing frameworks. In this paper, we present a new σ-order coflow scheduling solution, ONE-PARIS, an online semi-clairvoyant and semi-distributed implementation suitable to minimize the weighted CCT in production environments. We achieves this through ONE-PARIS scheduler for ordering coflows and a decentralized resource allocation mechanism, called Sync-Rate, enabling to respect the order of priority of coflows provided by ONE-PARIS and ensuring efficient synchronization between flows of the same coflow in order to free up bandwidth for low-priority flows. Extensive simulations on both synthetic and real traffics show that our proposed coflow scheduler outperforms other state-of-art schemes. | 10.1109/TNSM.2024.3395992 |
Qi Zhai, Limeng Dong, Chenxi Liu, Yong Li, Wei Cheng | Resource Management for Active RIS Aided Multi-Cluster SWIPT Cooperative NOMA Networks | 2024 | Early Access | Reconfigurable intelligent surfaces Resource management Power demand NOMA Array signal processing Energy efficiency Clustering algorithms Resource management Active RIS NOMA Cooperative transmission SWIPT | Active reconfigurable intelligent surface (RIS) has attracted a lot of attention due to its ability to drastically change the communication environment by adjusting the phase shift and amplifying the amplitude of signals. In this paper, we consider to apply the active RIS to enhance the performance of the multi-cluster cooperative non-orthogonal multiple access (CNOMA) system. Specifically, in terms of the power consumption, we first formulate a transmit power minimization problem by jointly optimizing the beamforming at the base station, power splitting ratio at cluster heads, power allocation in each cluster, and RIS matrices in direct transmission and cooperative transmission phases. Then, to improve the fair energy efficiency (EE), we solve a minimum EE maximization problem. To tackle the coupling of optimization variables, we propose the block coordinates descent (BCD) based algorithms. By applying the successive convex approximation (SCA), semi-definite relaxation (SDR), arithmetic-geometric mean (AGM) inequality, Schur complement, and convex upper bound substitution methods, the developed approaches are guaranteed to converge to local optimal solutions. Simulations results demonstrate that the proposed algorithms outperform the baseline schemes under passive RIS aided case, non-cooperation case, and no-RIS case in terms of power consumption and energy efficiency. It is also revealed that active RIS is not always superior to passive RIS schemes with a large number of RIS elements in terms of the system power consumption. | 10.1109/TNSM.2024.3395298 |
B. Naresh Kumar Reddy, Md. Zia Ur Rahman, Aime Lay-Ekuakille | Enhancing Reliability and Energy Efficiency in Many-Core Processors Through Fault-Tolerant Network-On-Chip | 2024 | Early Access | Task analysis Fault tolerant systems Fault tolerance Topology Runtime Network topology Benchmark testing Network-on-Chip Core Task mapping Communication Energy Performance FPGA board | This article presents a proposal for fault-tolerant task mapping on many-core processors to enhance system performance and reduce communication energy. The proposed algorithm maps tasks onto a 2-D mesh network-on-chip (NoC) and a modified NoC (MNoC) platform. The focus of this article is primarily on addressing permanent faults. In the scenario of a permanent fault within the mapped core, the algorithm also proposes a spare core placement strategy. This involves allocating the spare core based on considerations related to communication energy. The proposed task mapping algorithm underwent evaluation using various benchmarks, including multimedia and synthetic benchmarks. The results were then compared to those obtained from a 2-D mesh NoC and three related algorithms, all under the same task graph and NoC size. The simulation results showed that the proposed mapping algorithm on the modified NoC platform leads to improved performance and communication energy reductions when compared to the 2-D mesh NoC and the other three algorithms. To validate the proposed fault-tolerant task mapping algorithm on the modified NoC platform, A Field Programmable Gate Array (FPGA) was used to measure performance metrics such as application runtime, area, and on-chip power consumption in both faulty and non-faulty conditions. The hardware results indicated significant improvements when comparing the proposed FTTM on MNoC and 2-D NoC with existing approaches. | 10.1109/TNSM.2024.3394886 |
Xin Li, Yongli Zhao, Xiaosong Yu, Hua Wang, Wei Chen, Shuang Wang, Jie Zhang | Joint Bandwidth and Key On Demand (BKoD) Provisioning for Dynamic Service of Optical Transport Networks in F6G | 2024 | Early Access | Bandwidth Optical fiber networks Optical fibers Optical network units Security Encryption Resource management Bandwidth and key on demand dynamic service optical transport network F6G quantum key distribution resource allocation | In the sixth-generation fixed network (F6G), network security becomes an important topic. Encryption is an effective method to prevent network attacks and realize network security. Quantum key distribution (QKD) is a promising technology to effectively address the challenge by providing secret keys due to the laws of quantum physics. New services such as high immersion experience and holographic have the characteristics of time-varying bandwidth and requirements. The introduction of optical service unit (OSU) technology makes it possible to provide the exact bandwidth used by the service. In optical transport networks, a lightpath needs to be established before service transmission, and will be removed after service transmission. Signaling is used for lightpath establishment, removal, and bandwidth adjustment. Data information transmitted in data layer and signaling information transmitted in control layer are highly vulnerable to cyberattacks, such as eavesdropping. The supply of bandwidth and key resources need to be optimized to achieve secure and stable service transmission in optical networks. Hence, how to realize bandwidth and key on demand (BKoD) provisioning for dynamic services is a key problem. To improve the flexibility of bandwidth and key resource allocation and utilization, a QKD-secured OSU-based optical transport network can be deployed. In this paper, a novel QKD-secured OSU-based optical transport network architecture is proposed and a service aware dynamic resource provisioning (SADRP) algorithm is proposed to realize BKoD. The proposed architecture uses the QKD technique to provide keys for both signaling information and data information for the first time. The proposed algorithm supplies resources according to the dynamic demand of bandwidth and key, so as to achieve the balance between dynamic demand and static resource utilization. Simulations results show that compared with the benchmark algorithm, the SADRP algorithm reduces blocking probability by 4.16%, reduces bandwidth resource utilization rate by 4.39%, reduces key resource utilization rate by 3.48%, and improves security rate by 4.17%. | 10.1109/TNSM.2024.3387758 |
Ahmed Barnawi, Prateek Chhikara, Rajkumar Tekchandani, Neeraj Kumar, Bander Alzahrani | A Differentially Privacy Assisted Federated Learning Scheme to Preserve Data Privacy for IoMT Applications | 2024 | Early Access | Data privacy Data models Privacy Medical services Computational modeling Training Servers Computer Vision Differential Privacy Ensemble Learning Federated Learning Internet of Medical Things | The rapid development of Artificial Intelligence (AI) has had a significant impact on various industries, including healthcare. The Internet of Medical Things (IoMT) has played a vital role in this evolution. However, while AI has contributed to many benefits in healthcare, concerns about data privacy and security persist. To address these concerns, we propose a framework that combines Federated Learning (FL) and Differential Privacy (DP) to enhance data protection within IoMT. By integrating FL’s decentralized approach with DP’s mechanism to prevent data reconstruction from model outputs, we can improve data confidentiality. This integrated approach is used to develop and analyze high-performing Convolutional Neural Networks (CNNs) for detecting Tuberculosis using chest X-ray datasets. The framework undergo thorough performance evaluation, utilizing various metrics to establish its superiority over baseline models. The results demonstrate the effectiveness of our framework as a robust solution for secure and private AI applications in healthcare. | 10.1109/TNSM.2024.3393969 |
Huihui Wang, Chunping Wang, Kun Zhou, Duanyang Liu, Xiaoli Zhang, Hongbing Cheng | TEBChain: A Trusted and Efficient Blockchain-Based Data Sharing Scheme in UAV-Assisted IoV for Disaster Rescue | 2024 | Early Access | Disasters Blockchains Costs Authentication Collaboration InterPlanetary File System Computer crime disaster rescue blockchain data sharing PBFT consensus threshold signature | The destruction of communication infrastructure after a disaster makes it impossible for vehicles to timely transmit important data, such as casualty locations, road conditions and rescue demands, which brings great difficulties to ensure safe driving and efficient rescue. Some existing schemes have proposed the use of Unmanned Aerial Vehicles (UAVs) to assist data sharing in the Internet of Vehicles (IoV) to perform instant rescue missions. However, the untrusted network environment after the disaster and the mutual unbelief among rescue vehicles lead to potential security problems in data sharing between vehicles and UAVs. In addition, some selfish or malicious participants may disseminate meaningless or false data, which will not only waste valuable rescue resources in disaster areas but also may threaten the safety of rescue workers. To overcome these challenges, we propose TEBChain, a trusted and efficient data sharing scheme based on blockchain. In TEBChain, a blockchain-based lightweight framework is first designed to guarantee effective data sharing and record all abnormal behavior. Then, we present an improved key update mechanism based on the Boneh-Lynn-Shacham (BLS) threshold signature, which can ensure the trust of shared data among frequently moving vehicles. Furthermore, to facilitate consensus and reduce communication overhead, a lightweight and secure PBFT (LS-PBFT) consensus protocol is proposed to enable efficient rescue of vehicles and UAVs. Finally, the effectiveness and feasibility of our proposed TEBChain are validated through performance comparisons and simulation experiments. | 10.1109/TNSM.2024.3394162 |
Changhao Qiu, Bangbang Ren, Lailong Luo, Guoming Tang, Deke Guo | SFCPlanner: An Online SFC Planning Approach With SRv6 Flow Steering | 2024 | Early Access | Routing IP networks Planning Optimization Hardware Deep reinforcement learning Network function virtualization Segment routing reinforcement learning service function chain network function virtualization traffic engineering | Each flow usually needs to traverse a specific service function chain (SFC), which is composed of multiple network functions implemented through virtualization technology or hardware, before reaching their destinations. All network functions are deployed across commodity nodes inside a network environment. Each flow needs to change its default routing path to visit the corresponding SFC correctly. These changed routing paths will cause network load imbalance. Therefore, an intelligent routing planning method is needed to balance the traffic load while satisfying various SFC requirements of different flows. In this paper, we propose to leverage SRv6, a new routing technology, to centrally plan the routing path for each flow with any SFC request. We then present a general model of the SFC planning problem (SFCP), planning flows’ routing paths to minimize the maximum link utilization of the network, and prove that the problem is NP-hard. For this reason, we transform the SFCP problem into a graph theory optimization problem and propose SFCPlanner, an online SFC planning method based on deep reinforcement learning. Moreover, we design the node mask and incremental training mechanisms to make SFCPlanner achieve better performance. The experiment results show that our SFCPlanner can solve the SFCP problem in large-scale networks more precisely. It can reduce the maximum link utilization by 32% compared with the benchmark algorithm while ensuring each flow traverses the correct SFC. | 10.1109/TNSM.2024.3392945 |
Cheng Ren, Jiangping Zhang, Yu Wang, Yaxin Li | On Efficient VNF-FG Design in IoT Networks | 2024 | Early Access | Switches Heuristic algorithms Internet of Things Approximation algorithms Topology Standards Network topology Internet of Things directed acyclic graph virtualized network function forwarding graph design | In recent times, it has been witnessed that an increasing number of Internet connected devices impose an huge challenge on Internet of Thing (IoT) networks, which provides diverse and complex network services through edge computing empowered IoT terminals. A network service can be formally represented by Virtualized Network Function Forwarding Graph (VNF-FG) with the advent of Network Function Virtualization (NFV) technology. Previous researches mainly focus on VNF-FG embedding (VNF-FGE) and take VNF-FG as the input. In this paper, we investigate the design of VNF-FG, which is required to be a Directed Acyclic Graph (DAG) and achieved by the IoT terminal, in two scenarios. In static scenario, for a set of traffic flows arriving at the IoT terminal and requesting different network services, an ILP model Ps including loop prevention constraints is well formulated. An approximation algorithm AFGC with competitive ratio O(1+(|R|-1)α), α (0, 1) is then designed, which thoroughly search all key instances to run comprehensive loop break. In dynamic scenario for a flow request on the fly reaching an IoT terminal, an ILP model Pd and another approximation algorithm DFGU with a competitive ratio O(1 + |SCr||Fr| ) are developed, giving priority to reuse of existing topology to generate an augmented VNF-FG of minimum size. Simulation results indicate AFGC and DFGU outperform state-of-art work, and the gap between each of the two algorithms and their respective ILP models is marginal. | 10.1109/TNSM.2024.3392976 |
Yingchun Cui, Jinghua Zhu | MChain-SFFL: Multi-Chain Aggregation Privacy-Preserving for Server-Free Federated Learning | 2024 | Early Access | Privacy Computational modeling Servers Protection Costs Federated learning Cryptography Federated Learning Parallel Multi-chain Mask Information Privacy Protection | Federated Learning is a distributed learning paradigm that allows multiple organizations or devices to train a global model collaboratively in a privacy-preserving manner. However, there still exists privacy leakage risks due to the curious or dishonest server. In this paper, we propose a novel server-free federated learning paradigm named MChain-SFFL, which utilizes parallel multi-chain aggregation to mitigate privacy leakage risks and enhance convergence speed. First, MChain-SFFL randomly selects multiple users as chain heads. Then, MChain-SFFL utilizes parallel multi-chain communication mechanism to transmit the masked local model parameters. Finally, every chain head computes the model update for that chain and sends it to the other users. Upon receiving updates from all other chains, each user aggregates the received parameters to generate the model update for the current round. We validate the superiority of our method in accuracy and convergence speed on both image datasets and text datasets. Experimental results show that MChain-SFFL achieves superior privacy protection without impairing model accuracy and exhibits robustness to Non-IID data. | 10.1109/TNSM.2024.3393246 |
David Tipper, Amy Babay, Balaji Palanisamy, Prashant Krishnamurthy | Network Connectivity Resilience in Next Generation Backhaul Networks: Challenges and Future Opportunities | 2024 | Early Access | Wireless communication Resilience Hardware Ultra reliable low latency communication FCC Cellular networks Power system reliability Cellular Networks Outages Availability | Next generation cellular networks are expected to enable a wide range of new applications, increasing societal dependence on the network infrastructure and requiring a higher level of resilience than current networks. In this paper, we consider the challenges network operators face in providing end-to-end connections across the backhaul part of the cellular network in the face of equipment failures and power outages. In particular, we discuss the impact of the move to commodity hardware, disaggregation of the radio access network, edge computing, densification of the network, and the increased electric power requirements on resilience. Techniques and research directions for overcoming the challenges are presented. This includes thinking beyond methods for a single network operator including cooperative operator techniques and extending resilient overlays to the wireless edge. | 10.1109/TNSM.2024.3392857 |
Yan-Xia Chang, Qing Wang, Quan-Lin Li, Yaqian Ma, Chi Zhang | Performance and Reliability Analysis for PBFT-Based Blockchain Systems With Repairable Voting Nodes | 2024 | Early Access | Blockchains Protocols Reliability theory Queueing analysis Peer-to-peer computing Markov processes Maintenance engineering Practical Byzantine Fault Tolerance (PBFT) Blockchain Repairable voting nodes Markov process Queueing theory Performance evaluation Reliability | In a practical blockchain system based on the Practical Byzantine Fault Tolerance (PBFT) protocol, the voting nodes can fail at any time due to non-Byzantine errors, such as autonomous shutdowns, device crashes, and communication link failures caused by mobility or obstacles. These errors may cause voting nodes to exit the PBFT-based blockchain system unpredictably, resulting in a variable number of voting nodes available at any given time. To maintain optimal performance and consistency while adapting a PBFT-based blockchain system to this dynamic change, this paper proposes an extension to the PBFT protocol by introducing a repair process for failed nodes. The new PBFT-based blockchain system with repairable voting nodes is then analyzed for performance and reliability analysis by using multi-dimensional Markov processes, queueing theory, and the first passage time method. Additionally, we validate the accuracy of our theoretical findings by conducting numerical examples and simulation experiments. These experiments demonstrate that the introduction of a repair process can improve the performance and reliability of the PBFT-based blockchain system. Furthermore, we illustrate how various system parameters impact the performance measures of the PBFT-based blockchain system with repairable voting nodes. We hope that the methodology and results presented in this paper will establish a common framework for deriving theoretical analysis of existing PBFT-based blockchain systems and inspire future research efforts in this field. | 10.1109/TNSM.2024.3384506 |
Joongheon Kim, Soohyun Park, Soyi Jung, Carlos Cordeiro | Cooperative Multi-UAV Positioning for Aerial Internet Service Management: A Multi-Agent Deep Reinforcement Learning Approach | 2024 | Early Access | Autonomous aerial vehicles Reliability Wireless communication Millimeter wave communication Energy efficiency Quality of service Training Aerial Cellular Access Smart City Autonomous Vehicle Non-Terrestrial Network Multi-Agent Deep Reinforcement Learning Millimeter-Wave | This paper proposes a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration in mobile access applications where the UAVs work as mobile base stations. The primary objective of the proposed algorithm is to establish reliable mobile access networks for vehicle-to-everything (V2X) communications. This paper jointly considers energy-efficient UAV operation and reliable wireless communication services for realizing robust mobile access services. For the energy-efficient UAV operation, the reward function formulation of our proposed MADRL algorithm contains the features for UAV energy consumption models in order to realize efficient operations. Furthermore, for the reliable wireless communication services, the quality of service (QoS) requirements of individual users are considered as a part of reward function. Furthermore, this paper considers 60, GHz millimeter-wave (mmWave) mobile access for utilizing the benefits of i) ultra-wide-bandwidth for multi-Gbps high-speed communications and ii) high-directional communications for spatial reuse that is obviously good for avoiding interference among densely deployed users. Lastly, the comprehensive and data-intensive performance evaluation of the proposed MADRL-based algorithm for multi-UAV positioning is conducted. The results of these evaluations demonstrate that the proposed algorithm outperforms other existing algorithms. | 10.1109/TNSM.2024.3392393 |
Friedrich Altheide, Simon Buttgereit, Michael Rossberg | Increasing Resilience of SD-WAN by Distributing the Control Plane [Extended Version] | 2024 | Early Access | Logic gates Wide area networks Bandwidth Robustness Uplink Scalability Traffic control SD-WAN SDN Robustness Distributed Systems | Modern WAN interconnects utilize SD-WAN to automatically respond to network changes and improve link utilization, latency, and availability. Therefore, they incorporate controllers with a centralized view, which collect network state from managed gateways, calculate suitable forwarding actions, and distribute them accordingly. However, this limits the robustness and availability of the network control plane, especially in the event of node or partial network outages. In this paper, we propose a distributed and highly robust SD-WAN control plane without any central or regional controller. Our solution can handle arbitrary device failures as well as network partitioning. The distributed forwarding decisions are based on user-defined, dynamically evaluated path cost functions, and consider not only path quality but also quality fluctuations. The evaluation shows that our approach can handle several thousand SD-WAN gateways and hundreds of network policies in terms of computation. Further, the communication overhead introduced due to its distributed architecture is discussed and shown to be negligible compared to a central approach. This paper is an extended version of our work published in altheide2023. It describes the information transmitted between sites as well as a strategy for deploying policies, discusses approaches reducing communication bandwidth, introduces grouping of multiple flows without requiring explicit coordination, and provides a detailed analysis of the bandwidth required. | 10.1109/TNSM.2024.3386962 |
Yuanyuan Fu, Jian Xu | LogTransformer: Transforming IT System Logs Into Events Using Tree-Based Approach | 2024 | Early Access | Servers Vectors Source coding Task analysis Robustness Maintenance Software systems event extraction log analysis log parsing similarity metric system maintenance | As an important outcome of complex IT systems in operation, logs provide valuable information for system operation and maintenance. Log event (or template) extraction plays a vital role in log analysis, as its accuracy significantly impacts follow-up tasks such as log anomaly detection and event pattern discovery. Despite achieving high accuracy on specific system logs, existing log event extraction approaches still struggle with low accuracy and instability when handling logs from heterogeneous systems or logs with variable-length parameters. To address these issues, this paper proposes LogTransformer, an online event extraction approach based on a tree structure. A tree-based log content parsing approach is proposed to perform log event extraction by comparing the similarity between a log tree representing an incoming log message and an event tree representing a specific log template. Extensive experiments are conducted on sixteen benchmark log datasets to evaluate the effectiveness, robustness, and efficiency of the proposed approach. The experimental results demonstrate that an average accuracy exceeds 90%, surpassing the state-of-the-art online log parser, Drain. | 10.1109/TNSM.2024.3391290 |
Fahime Khoramnejad, Aisha Syed, W. Sean Kennedy, Melike Erol-Kantarci | Energy and Delay Aware General Task Dependent Offloading in UAV-Aided Smart Farms | 2024 | Early Access | Task analysis Internet of Things Autonomous aerial vehicles Optimization Smart agriculture Delays Servers Edge computing compound-action reinforcement learning graph neural networks offloading task dependency | Edge computing offers a promising solution to enhance network reliability. In this study, we investigate the integration of mobile edge computing (MEC) technology and unmanned aerial vehicles (UAVs) within the context of smart agriculture. Smart agriculture relies on resource-constrained Internet of Things (IoT) devices for local environmental monitoring and data collection. These IoT devices send the collected data to UAVs for analysis. A central theme of this work is the focus on the applications generated by each UAV and the consideration of their topology to derive our optimization algorithm. To tackle these challenges, we propose harnessing the computational and power resources of UAVs and MEC at the network’s edge to offload and execute resource-intensive tasks in UAV-MEC-assisted networks. Our research focuses on the joint optimization of power allocation and task offloading in these wireless networks. Central to our investigation is the problem of minimizing the energy-time cost (ETC) for the UAVs, considering the interdependencies among tasks. To address this complex problem efficiently, we introduce graph convolutional neural networks (GCNs) and reinforcement learning (RL)-based techniques. We employ a directed acyclic graph (DAG) to model task interdependencies, with GCNs characterizing the DAG. Our approach incorporates an actor-critic method with embedding layers, trained using the compound-action actor-critic (CA2C) algorithm. Our findings reveal a significant improvement in minimizing both delay and energy consumption, with a 27% percent reduction in delay and a 45% reduction in consumed energy for executing complex, interdependent tasks. | 10.1109/TNSM.2024.3391664 |
Yuping Lai, Yiying Yu, Wenbo Guan, Lijuan Luo, Jing Fan, Nanrun Zhou, Yuan Ping | A Lightweight Intrusion Detection System Using a Finite Dirichlet Mixture Model With Extended Stochastic Variational Inference | 2024 | Early Access | Computational modeling Training Feature extraction Genetic algorithms Stochastic processes Mixture models Vectors Network Security Intrusion Detection System Mixture Modeling Dirichlet Distribution Bayesian Estimation Extended Stochastic Variational Inference | With the rapid development of the internet worldwide, network security issues are becoming increasingly prominent. Network intrusion detection systems (NIDSs) play a vital role in ensuring computer network security due to their ability to identify potential network threats. Despite considerable research efforts, deploying NIDSs on resource-constrained devices has been challenging. To reduce the imposed computational cost and model storage requirements, in this paper, we propose a novel lightweight NIDS model. In this model, patterns of normal and malicious actions are learned via a finite Dirichlet mixture model (DMM) in the context of the extended stochastic variational inference (ESVI) framework. With the proposed method, both the parameter estimation and model selection processes can be simultaneously addressed in a unified Bayesian framework. A great number of experiments conducted on three publicly available datasets demonstrate that the proposed model not only achieves comparable classification performance to that of detection models based on several well-studied finite mixture modeling, traditional machine learning (ML) and promising deep learning (DL) algorithms but also significantly reduces the required training and detection time. Extensive experimental results validate that the proposed model is a feasible and efficient lightweight intrusion detection model. | 10.1109/TNSM.2024.3391250 |
Chen Chen, Jiabao Si, Huan Li, Wei Han, Neeraj Kumar, Stefano Berretti, Shaohua Wan | A High Stability Clustering Scheme for the Internet of Vehicles | 2024 | Early Access | Magnetic heads Clustering algorithms Heuristic algorithms Measurement Delays Stability criteria Optimization the Internet of Vehicles Cluster Head Selection Scheme Machine Learning | In existing research on cluster head selection schemes in the Internet of Vehicles (IoV), designing a stable cluster structure poses a significant challenge. Choosing a centrally-located cluster head that can respond rapidly is crucial for meeting various requirements. To address the aforementioned challenges, this paper introduces a machine learning-based IoV cluster head selection scheme (HSCS). We introduce a new metric termed N-cycle Average Virtual Cluster Delay (XTn) for appropriate cluster head selection. To accommodate the high dynamism of vehicles, a machine learning model is integrated to predict cluster head selection metrics across different periods, and a set of cluster head selection guidelines is formulated. Experimental results demonstrate that our proposed HSCS ensures a relatively low average intra-cluster delay while maintaining a longer cluster head retention time, and it exhibits commendable robustness. | 10.1109/TNSM.2024.3390117 |
Marco Martalò, Giovanni Pettorru, Luigi Atzori | A Cross-Layer Survey on Secure and Low-Latency Communications in Next-Generation IoT | 2024 | Early Access | Internet of Things Surveys Security Protocols Industrial Internet of Things 6G mobile communication Next generation networking 6G Internet of Things (IoT) Industrial IoT (IIoT) privacy security low latency QUIC | The last years have been characterized by strong market exploitation of the Internet of Things (IoT) technologies in different application domains, such as Industry 4.0, smart cities, and eHealth. All the relevant solutions should properly address the security issues to ensure that sensor data and actuators are not under the control of malicious entities. Additionally, many applications should at the same time provide low-latency communications, as in the case for instance of remote control of industrial robots. Low latency and security are two of the most important challenges to be addressed for the successful deployment of IoT applications. These issues have been analyzed by several scientific papers and surveys that appeared in the last decade. However, few of them consider the two challenges jointly. Moreover, the security aspects are primarily investigated only in specific application domains or protocol levels and the latency issues are typically investigated only at low layers (e.g., physical, access). This paper addresses this shortcoming and provides a systematic review of state-of-the-art solutions for providing fast and secure IoT communications. Although the two requirements may appear to be in contrast to each other, we investigate possible integrated solutions that minimize device connection and service provisioning. We follow an approach where the proposals are reviewed by grouping them based on the reference architectural layer, i.e., access, network, and application layers. We also review the works that propose promising solutions that rely on the exploitation of the QUIC protocol at the higher levels of the protocol stack. | 10.1109/TNSM.2024.3390543 |
Xiaojuan Wang, Yiqing Luo, Mingshu He, Xinlei Wang | A Semantic Detection Method for Network Flows With Global and Generalized Nature | 2024 | Early Access | Feature extraction Semantics Correlation Threat assessment Analytical models Transformers Network topology threat detection Semantic analysis topological relationship heterogeneous graph graph convolutional network transformer encoder | Network threat detection and identification are essential tasks in the defense of cyberspace. However, current network threat detection methods have limitations such as narrow feature extraction, targeted feature effects, and limited generalization performance. Therefore, there is a need for a more comprehensive understanding and description of network behavior. As a result, we propose a global and generalized method for semantic detection of network flow to enhance the definition of flow data and representation of network behavior. To improve the problem of narrow feature range in existing methods, this paper designs three feature embedding methods that represent global, temporal, and local semantic correlations from both temporal and spatial dimensions: global embedding, position embedding, and learning embedding. In order to overcome the problem of existing methods only targeting specific behaviours, this article focuses on constructing global correlation features to replace the detection mode of building an inherent feature set. By utilizing text analysis features, we extract global embedding features containing network flow relationship information by constructing a topology heterogeneous graph between flows and bytes. This is combined with position embedding and learning embedding to complete data detection and behavior classification through input into the transformer encoder. We validated the effectiveness of our method in three scenarios: the Internet, the Internet of Things, and encryption. The final experimental results demonstrated that our proposed method outperformed existing advanced models. Furthermore, after incorporating global embedding representing international correlation relationships, the model’s classification accuracy was further improved. | 10.1109/TNSM.2024.3390180 |