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21st USENIX Symposium on Networked Systems Design and Implementation, NSDI 2024

作者： Li, Tianlong Song, Tian Yang, Yating School of Computer Science and Technology China School of Cyberspace Science and Technology Beijing Institute of Technology China

ISBN: (纸本)9781939133397

Named Data Networking (NDN) shifts the network from host-centric to data-centric with a clean-slate design, in which packet forwarding is based on names, and the data plane maintains per-packet state. Different forwarders have been implemented to provide NDN capabilities for various scenarios, however, there is a lack of a network stack that is integrated with operating systems (OS) for general purpose. Designing a stateful and entirely name-based protocol stack in OS kernel remains a challenge due to three factors: (i) an in-kernel name resolution architecture for packet demultiplexing is necessary, (ii) an entirely name-based stack requires to be compatible with the current address (MAC/IP/port)-based architecture in OS kernel, and (iii) maintaining per-packet state introduces a trade-off between performance and resource consumption. In this paper, for the first time, we take NDN into OS kernel by proposing iStack, an Information-Centric Networking (ICN) protocol stack. The main innovations of iStack are threefold. First, we propose a name resolution architecture to support both network-layer forwarding and local packet demultiplexing. Second, a two-layer face system is proposed to provide abstraction of address-based network interfaces. Third, we design socket-compatible interfaces to keep the uniformity of current network stack in OS. Besides, we design compact forwarding data structures for fast packet processing with low memory footprint. We have implemented prototypes on multiple platforms. The evaluation results show that iStack achieves 6.50 Gbps throughput, outperforming the NDN-testbed forwarder by a factor of 16.25x, and reduces 46.08% forwarding latency for cached packets with its in-kernel packet caching. iStack is not just another forwarder for NDN, but a step forward for practical development of ICN. © 2024 Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation, NSDI 2024. All rights reserved.

关键词： Network layers

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Research on Network Traffic Classification Based on Graph Neural Network

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2043-2050页

作者： Han, Yue Dai, Hong University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China

Network traffic classification is a critical concern in network security and management, essential for accurately differentiating among various network applications, optimizing service quality, and improving user experience. The exponential increase in worldwide Internet users and network traffic is continuously augmenting the diversity and complexity of network applications, rendering the Internet environment increasingly intricate and dynamic. Conventional machine learning techniques possess restricted processing abilities for network traffic attributes and struggle to address the progressively intricate traffic classification tasks in contemporary networks. In recent years, the swift advancement of deep learning technologies, particularly Graph Neural Networks (GNN), has yielded significant improvements in network traffic classification. GNN can capture the structured information among network nodes and extract the latent features of network traffic. Nonetheless, current network traffic classification models continue to exhibit deficiencies in the thoroughness of feature extraction. To tackle the problem, this research proposes a method for constructing traffic graphs utilizing numerical similarity and byte distance proximity by exploring the latent correlations among bytes, and it constructs a model, SDA-GNN, based on Graph Isomorphic Networks (GIN) for the categorization of network traffic. In particular, the Dynamic Time Warping (DTW) distance is employed to evaluate the disparity in byte distributions, a channel attention mechanism is utilized to extract additional features, and a Long Short-Term Memory Network (LSTM) enhances the stability of the training process by extracting sequence characteristics. Experimental findings on two actual datasets indicate that the SDA-GNN model surpasses other baseline techniques across multiple assessment parameters in the network traffic classification task, achieving classification accuracy enhancements of 2.19% and 1.49%

关键词： Long short-term memory

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An efficient multi-objective task scheduling in edge computing using adaptive honey badger optimisation

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International Journal of Web Engineering and technology 2024年第2期19卷 110-126页

作者： Nagalakshmi, Bantupalli Subramanian, Sumathy School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Task scheduling, which is important in cloud computing, is one of the most challenging issues in this area. Hence, an efficient and reliable task scheduling approach is needed to produce more efficient resource employment. So, a multi-objective-based task scheduling for edge computing is suggested in this study. This paper develops the adaptive honey badger optimisation algorithm (AHBA) to accomplish this goal. The lack of population, the original honey badger algorithm (HBO) has the issue of becoming trapped in local optima. To maintain population variety and improve convergence towards the ideal solution, HBO is combined with the opposition-based learning technique (OBL). Based on makespan, cost, energy consumption, and resource usage, the multi-objective function is created. According to simulation results, the proposed approach has a lot of potential in this field. Java and cloud Simulator are used to implement the suggested model. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Energy utilization

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Lay CO:Achieving Least Lossy Accuracy for Most Efficient RRAM-Based Deep Neural Network Accelerator via Layer-Centric Co-Optimization

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计算机科学技术学报（英文版） 2023年第2期38卷 328-347页

作者： Shao-Feng Zhao Fang Wang Bo Liu Dan Feng Yang Liu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen 518057China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China

Resistive random access memory(RRAM)enables the functionality of operating massively parallel dot prod-ucts and ***-based accelerator is such an effective approach to bridging the gap between Internet of Things devices'constrained resources and deep neural networks'tremendous *** to the huge overhead of Analog to Digital(A/D)and digital accumulations,analog RRAM buffer is introduced to extend the processing in analog and in *** analog RRAM buffer offers potential solutions to A/D conversion issues,the energy consumption is still challenging in resource-constrained environments,especially with enormous intermediate data ***,criti-cal concerns over endurance must also be resolved before the RRAM buffer could be frequently used in reality for DNN in-ference *** we propose LayCO,a layer-centric co-optimizing scheme to address the energy and endurance con-cerns altogether while strictly providing an inference accuracy *** relies on two key ideas:1)co-optimizing with reduced supply voltage and reduced bit-width of accelerator architectures to increase the DNN's error tolerance and achieve the accelerator's energy efficiency,and 2)efficiently mapping and swapping individual DNN data to a correspond-ing RRAM partition in a way that meets the endurance *** evaluation with representative DNN models demonstrates that LayCO outperforms the baseline RRAM buffer based accelerator by 27x improvement in energy effi-ciency(over TIMELY-like configuration),308x in lifetime prolongation and 6x in area reduction(over RAQ)while main-taining the DNN accuracy loss less than 1％.

关键词： deep learning accelerator resistive random access memory (RRAM) energy efficiency error tolerance

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Tensor-Based Factorial Hidden Markov Model for Cyber-Physical-Social Services

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IEEE Transactions on Services Computing 2025年第3期18卷 1825-1837页

作者： Lu, Zhixing Yang, Laurence T. Azman, Azreen Zhang, Shunli Zhou, Fang Hainan University School of Computer Science and Technology Hainan China Universiti Putra Malaysia Faculty of Computer Science and Information Technology Serdang Malaysia Zhengzhou University School of Computer and Artificial Intelligence School of Software Engineering Zhengzhou China Huazhong University of Science and Technology School of Computer Science and Technology Huazhong China St. Francis Xavier University Department of Computer Science Canada

With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score. © 2008-2012 IEEE.

关键词： Viterbi algorithm

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From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks

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IEEE Internet of Things Magazine 2024年第3期7卷 30-37页

作者： Wen, Jinbo Nie, Jiangtian Kang, Jiawen Niyato, Dusit Du, Hongyang Zhang, Yang Guizani, Mohsen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Computer Science and Engineering Nanyang Technological University China School of Automation Guangdong University of Technology China United Arab Emirates

Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, enabling more efficient and intelligent IoT applications, such as smart surveillance and voice assistants. In this article, we present the concept of GIoT and conduct an exploration of its potential prospects. Specifically, we first overview four GAI techniques and investigate promising GIoT applications. Then, we elaborate on the main challenges in enabling GIoT and propose a general GAI-based secure incentive mechanism framework to address them, in which we adopt Generative Diffusion Models (GDMs) for incentive mechanism designs and apply blockchain technologies for secure GIoT management. Moreover, we conduct a case study on modern Internet of Vehicle traffic monitoring, which utilizes GDMs to generate effective contracts for incentivizing users to contribute sensing data with high quality. Numerical results demonstrate the superiority of the proposed scheme. Finally, we suggest several open directions worth investigating for the future popularity of GIoT. © 2018 IEEE.

关键词： Internet of things

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An Asymmetric Searchable Encryption Scheme Supporting Shortest Distance Query in Internet of Vehicles

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IEEE Internet of Things Journal 2025年第13期12卷 25867-25878页

作者： Chen, Zhenhua Fan, Siying Jin, Hao Zhang, Jing Li, Zhen Su, Jing Xi'An University of Science and Technology School of Computer Science and Technology Xi'an710054 China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China

With the advent of Internet technologies, the concept of conventional vehicular ad hoc network is gradually migrating into the Internet of Vehicles (IoV). Meanwhile, as a common data structure composed of vertices and edges, graph commonly used to represent road network typologies, are also outsourced to Internet servers for the convenience of people in IoV accessing the road network anytime and anywhere. However, it introduces much potential risks on people's privacy as the Internet servers are often assumed semi-honest. As an essential component in IoV, privacy-preserving shortest distance query has been researched many years in the cryptographic community. Unfortunately, most of solutions are designed under symmetric key setting and employ some off-the-shelf cryptographic primitives. They are impractical in the multi-user IoV environment due to an inherent weakness-key agreement required between each pair of a graph data provider and a user since the symmetric key encryption was born. Moreover, these off-the-shelf primitives ask multiple Internet servers or graph data providers to interact in the test process in order to assist one Internet server returning the shortest distance to users, both of which thereby raise a significant communication cost. To overcome these drawbacks, in this paper we propose an asymmetric searchable encryption scheme supporting shortest distance query, which is quite suitable for multi-user IoV environment since the key agreement is no longer required between each pair of a graph data provider and a user. In particular, we present a new cryptographic primitive instead of leveraging any ready-made one, that enables only one server in IoV to be required for returning the final query result - shortest distance to users without any interaction with others, thereby reducing the communication cost. Finally, a strict mathematical security proof is offered as well as some security attacks are analyzed, followed by an experimental evaluation an

关键词： Vehicular ad hoc networks

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Advancing differential diagnosis: a comprehensive review of deep learning approaches for differentiating tuberculosis, pneumonia, and COVID-19

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Multimedia Tools and Applications 2025年第13期84卷 11871-11906页

作者： Kansal, Kajal Chandra, Tej Bahadur Singh, Akansha School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

In the realm of medical diagnostics, particularly in differential diagnosis, where differentiating between illnesses or ailments with comparable symptoms is essential, deep learning has gained importance. Recent developments in deep learning have demonstrated considerable promise for revolutionizing medical diagnostics by using the ability of artificial intelligence (AI) to accurately interpret radiological images. We examine the most cutting-edge deep learning techniques currently being utilized for the differential diagnosis of tuberculosis, pneumonia, and COVID-19 in this in-depth review. The study presents an in-depth critical review of several SOTA (state-of-the-art) studies used for differential diagnosis of different respiratory abnormalities like TB, Pneumonia, and COVID-19. In addition, an overview of various approaches, datasets employed in each method, various diagnosis tests, used assessment measures, and obtained performance is summarized and comprehensively compared to assist future research. We suggest a pathway for future research and development of deep learning solutions for differential diagnosis by critically analyzing the current literature and outlining the limitations and potential in this sector. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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FLAG: frequency-based local and global network for face forgery detection

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Multimedia Tools and Applications 2025年第2期84卷 647-663页

作者： Zhou, Kai Sun, Guanglu Wang, Jun Wang, Jiahui Yu, Linsen School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Department of Information Engineering and Mathematics University of Siena Siena53100 Italy

Deepfake detection aims to mitigate the threat of manipulated content by identifying and exposing forgeries. However, previous methods primarily tend to perform poorly when confronted with cross-dataset scenarios. To address the above issue, we propose an innovative hybrid network called the Frequency-based Local and Global (FLAG) network to explore local and global information with the help of frequency-domain cues for better generalization capability. In consideration of the fact that forged faces often exhibit flaws in the frequency domain, we design a Frequency-based Attention Enhancement Module (FAEM) to enhance the aggregation of CNN and Vision Transformer (ViT). In this design, local features from CNN are attentively enhanced by selected frequency coefficients in FAEM, facilitating generalizable global features learning by the ViT module. The effectiveness of the proposed method is validated via numerous experiments and the generalization performance is improved under cross-dataset scenarios. Especially, the proposed method have obtained an AUC of 99.26% and an ACC of 96.56% using intra-dataset experimental results on FaceForensics++ (C23). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Face recognition

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Histogram Matching-Based Reversible Data Hiding for Intelligent Transportation Applications

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IEEE Internet of Things Journal 2025年第13期12卷 24599-24614页

作者： Zhang, Kexin Yao, Heng Yang, Xin Qin, Chuan University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

In today's intelligent transportation systems, the effectiveness of image-based analysis relies heavily on image quality. To enhance images while preserving reversibility, this paper proposes a histogram matching-based reversible data hiding (HMRDH) method. The proposed approach ensures high embedding capacity by incorporating histogram shifting from reversible data hiding with contrast enhancement (RDHCE). Unlike traditional RDHCE methods that are limited to achieving histogram equalization, our method constrains the sequence of bin adjustments to achieve flexible histogram matching. The algorithm first evaluates bin heights to assign roles, then iteratively selects and adjusts corresponding bins while embedding *** prevent deviation from the target histogram, this method calculates the root mean square error after each iteration to ensure that the adjustment is retained only if it improves the matching accuracy. When necessary, the original image and embedded information can be fully recovered. Extensive experiments demonstrate the superiority of the proposed method in visual quality, embedding capacity, and adaptability. © 2025 IEEE.

关键词： Histograms Internet of Things Accuracy Image quality Brightness Servers Image restoration Distortion Biomedical imaging Transportation

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