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arXiv 2024年

作者： Sun, Haili Huang, Yan Han, Lansheng Fu, Cai Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Accurate detection and diagnosis of abnormal behaviors such as network attacks from multivariate time series (MTS) are crucial for ensuring the stable and effective operation of industrial cyber-physical systems (CPS). However, existing researches pay little attention to the logical dependencies among system working states, and have difficulties in explaining the evolution mechanisms of abnormal signals. To reveal the spatio-temporal association relationships and evolution mechanisms of the working states of industrial CPS, this paper proposes a fine-grained adaptive anomaly diagnosis method (i.e. MAD-Transformer) to identify and diagnose anomalies in MTS. MAD-Transformer first constructs a temporal state matrix to characterize and estimate the change patterns of the system states in the temporal dimension. Then, to better locate the anomalies, a spatial state matrix is also constructed to capture the inter-sensor state correlation relationships within the system. Subsequently, based on these two types of state matrices, a three-branch structure of series-temporal-spatial attention module is designed to simultaneously capture the series, temporal, and space dependencies among MTS. Afterwards, three associated alignment loss functions and a reconstruction loss are constructed to jointly optimize the model. Finally, anomalies are determined and diagnosed by comparing the residual matrices with the original matrices. We conducted comparative experiments on five publicly datasets spanning three application domains (service monitoring, spatial and earth exploration, and water treatment), along with a petroleum refining simulation dataset collected by ourselves. The results demonstrate that MAD-Transformer can adaptively detect fine-grained anomalies with short duration, and outperforms the state-of-the-art baselines in terms of noise robustness and localization performance. © 2024, CC BY.

关键词： Cyber Physical system

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An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition

arXiv

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arXiv 2024年

作者： Xia, Yizhang Song, Shihao Hou, Zhanglu Xu, Junwen Zou, Juan Liu, Yuan Yang, Shengxiang Hunan Engineering Research Center of Intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China The Centre for Computational Intelligence School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Hand gesture recognition (HGR) based on multimodal data has attracted considerable attention owing to its great potential in applications. Various manually designed multimodal deep networks have performed well in multimodal HGR (MHGR), but most of existing algorithms require a lot of expert experience and time-consuming manual trials. To address these issues, we propose an evolutionary network architecture search framework with the adaptive multimodel fusion (AMF-ENAS). Specifically, we design an encoding space that simultaneously considers fusion positions and ratios of the multimodal data, allowing for the automatic construction of multimodal networks with different architectures through decoding. Additionally, we consider three input streams corresponding to intra-modal surface electromyography (sEMG), intra-modal accelerometer (ACC), and inter-modal sEMG-ACC. To automatically adapt to various datasets, the ENAS framework is designed to automatically search a MHGR network with appropriate fusion positions and ratios. To the best of our knowledge, this is the first time that ENAS has been utilized in MHGR to tackle issues related to the fusion position and ratio of multimodal data. Experimental results demonstrate that AMF-ENAS achieves state-of-the-art performance on the Ninapro DB2, DB3, and DB7 datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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A Survivable Routing Algorithm with Hybrid Protection in Elastic Optical Networks

A Survivable Routing Algorithm with Hybrid Protection in Ela...

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Mobile Internet, Cloud Computing and Information security (MICCIS), International Conference on

作者： Hankun Zeng Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

Spectral efficiency is a crucial factor in the survivability of multi-class traffic in elastic optical networks (EONs). In this paper, we propose a spectrum-efficient hybrid protection scheme, namely dedicated path protection (DPP) and shared backup path protection (SBPP), to address the survivability issue in EONs. Meanwhile, we design a survivable routing, modulation level, and spectrum assignment (RMLSA) algorithm to achieve hybrid protection in EONs. The simulation results demonstrate that the proposed algorithm has a lower blocking rate and Spectrum occupation ratio.

关键词： Time-frequency analysis Spectral efficiency Simulation Modulation Information security Optical fiber networks Routing

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Byzantine-Robust Federated Learning on Non-IID data via Inversing Artificial Gradients

Byzantine-Robust Federated Learning on Non-IID Data via Inve...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Minyang Li Xiangyun Tang Tao Zhang Guangyuan Liu Yu Weng Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Minzu University of China China School of Software Engineering Beijing Jiaotong University China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have proposed solutions to improve the robustness of FL systems. However, these robust approaches have not effectively defended against Byzantine attacks from malicious clients in non-Independent and Identically Distributed (non-IID) environments, decreasing overall training accuracy. In this work to address this issues, we propose a new defence framework that aims to enhance model accuracy against Byzantine attacks from malicious clients in non-IID environments. In our approach, the central server performs the Inverse Deep Learning Gradient attack using the gradient data submitted by users in each round, obtaining an inversed artificial gradient. We then assess the squared L2 norm difference between this inversed gradient and the actual gradients to detect malicious clients. Simultaneously, we assign weights to each client involved in the aggregation based on differences in the squared L2 norm, aiming to minimize the impact of malicious actors on the overall model. We have experimentally evaluated our method and demonstrated its ability to maintain training accuracy under attack in non-IID environments using standard datasets.

关键词： Training Accuracy Federated learning Distributed databases Robustness data models Servers Global communication Standards Faces

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Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

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ACM Transactions on Internet Technology 2021年第1期22卷 1-23页

作者： Xu, Jia Zhou, Yuanhang Chen, Gongyu Ding, Yuqing Yang, Dejun Liu, Linfeng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China Colorado School of Mines 1500 Illinois St. GoldenCO80401 United States

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion (BFTD) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average. © 2021 Association for Computing Machinery.

关键词： Crowdsourcing

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Improvement of the square-root low bounds on the minimum distances of BCH codes and Matrix-product codes

arXiv

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arXiv 2025年

作者： Wang, Xiaoqiang Li, Liuyi Wu, Yansheng Zheng, Dabin Lu, Shuxian Hubei Key Laboratory of Applied Mathematics Key Laboratory of Intelligent Sensing System and Security Ministry of Education Faculty of Mathematics and Statistics Hubei University Wuhan430062 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China National Mobile Communications Research Laboratory Southeast University Nanjing211111 China

The task of constructing infinite families of self-dual codes with unbounded lengths and minimum distances exhibiting square-root lower bounds is extremely challenging, especially when it comes to cyclic codes. Recently, the first infinite family of Euclidean self-dual binary and nonbinary cyclic codes, whose minimum distances have a square-root lower bound and have a lower bound better than square-root lower bounds are constructed in [6] for the lengths of these codes being unbounded. Let q be a power of a prime number and Q = q2. In this paper, we first improve the lower bounds on the minimum distances of Euclidean and Hermitian duals of BCH codes with length (formula presented) over (formula presented) and (formula presented) over FQ in [11], [13], [31] for the designed distances in some ranges, respectively, where (formula presented). Then based on matrix-product construction and some lower bounds on the minimum distances of BCH codes and their duals, we obtain several classes of Euclidean and Hermitian self-dual codes, whose minimum distances have square-root lower bounds or a square-root-like lower bounds. Our lower bounds on the minimum distances of Euclidean and Hermitian self-dual cyclic codes improved many results in [6]. In addition, our lower bounds on the minimum distances of the duals of BCH codes are almost qs − 1 or q times that of the existing lower bounds. © 2025, CC BY.

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Location and Lightpath Selection in Multi-Domain Optical data Center Networks

Location and Lightpath Selection in Multi-Domain Optical Dat...

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Mobile Internet, Cloud Computing and Information security (MICCIS), International Conference on

作者： Changle Xu Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

In the era of booming fifth-generation fixed networks (F5G), the service demands for high bandwidth continue to grow. Meanwhile, in multi-domain network scenarios, service requests are vulnerable to physical layer attacks initiated by malicious clients. In this paper, a risk-aware heuristic algorithm is proposed to address the problem of lightpath provisioning in multi-domain elastic optical data center networks (EODCNs). Simulation results show that the blocking probability of the proposed algorithm is lower than the comparison algorithm, 8.2% and 8.8% lower in NSFNET topology and US-Backbone topology, respectively.

关键词： Integrated optics Multiplexing data centers Network topology Optical design Simulation Optical fiber networks

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Segment Routing based In-Band Network Telemetry in IPv6 over Optical Networks

Segment Routing based In-Band Network Telemetry in IPv6 over...

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Mobile Internet, Cloud Computing and Information security (MICCIS), International Conference on

作者： Wei Liu Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

With the rise of automated network operations, network telemetry has become increasingly important. Optical networks play a key role in modern communication systems because of their high bandwidth, low latency, and low loss. To better manage and monitor Internet Protocol version 6 (IPv6) over optical networks, programmable data planes (PDP) are used to achieve flexible data collection. Meanwhile, to solve the scalability problem of the control plane in the software-defined network (SDN), segment routing over IPv6 (SRv6) is introduced. By programming the source node on the path, SRv6 wisely instructs packets to collect the appropriate telemetry data at the segment nodes to balance the trade-off between bandwidth usage and monitoring coverage. To efficiently achieve the observability of IPv6 over optical networks, we model the system and propose an SRv6-based in-band network telemetry (INT) algorithm. Simulation results verify the performance of our algorithm.

关键词： Protocols Simulation Bandwidth Optical fiber networks Routing Universal Serial Bus Topology

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MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction

arXiv

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arXiv 2024年

作者： Qiao, Xiaoyu Li, Weisheng Wang, Guofen Huang, Yuping Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China College of Computer and Information Science Chongqing Normal University Chongqing China

Deep unfolding networks (DUNs) have demonstrated significant potential in accelerating magnetic resonance imaging (MRI). However, they often encounter high computational costs and slow convergence rates. Besides, they struggle to fully exploit the complementarity when incorporating multiple priors. In this study, we propose a multi-prior collaborative DUN, termed MCU-Net, to address these limitations. Our method features a parallel structure consisting of different optimization-inspired subnetworks based on low-rank and sparsity, respectively. We design a gates-controlled spatial attention module (GSAM), evaluating the relative confidence (RC) and overall confidence (OC) maps for intermediate reconstructions produced by different subnetworks. RC allocates greater weights to the image regions where each subnetwork excels, enabling precise element-wise collaboration. We design correction modules to enhance the effectiveness in regions where both subnetworks exhibit limited performance, as indicated by low OC values, thereby obviating the need for additional branches. The gate units within GSAMs are designed to preserve necessary information across multiple iterations, improving the accuracy of the learned confidence maps and enhancing robustness against accumulated errors. Experimental results on multiple datasets show significant improvements on PSNR and SSIM results with relatively low FLOPs compared to cutting-edge methods. Additionally, the proposed strategy can be conveniently applied to various DUN structures to enhance their performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Fine-Grained Prompt Learning for Face Anti-Spoofing 24

Fine-Grained Prompt Learning for Face Anti-Spoofing

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32nd ACM International Conference on Multimedia, MM 2024

作者： Hu, Xueli Liu, Huan Yuan, Haocheng Fu, Zhiyang Luo, Yizhi Zhang, Ning Zou, Hang Gan, Jianwen Zhang, Yuan Key Laboratory of Cyberspace Security Ministry of Education Zhengzhou China Beijing Jiaotong University Beijing China Macau University of Science and Technology China Sichuan Intelligent Transportation System Management Co. Ltd. Chengdu China Institute of Forensic Science Beijing China China Telecom Research Institute Beijing China Wuzhou University Wuzhou China China Telecom Research Institute Shanghai China

ISBN: (纸本)9798400706868

There has been an increasing focus on domain-generalized (DG) face anti-spoofing (FAS). However, existing methods aim to project a shared visual space through adversarial training, making exploring the space without losing semantic information challenging. We investigate the DG inadequacies resulting from classifier overfitting to a significantly different domain distribution. To address this issue, we propose a novel Fine-Grained Prompt Learning (FGPL) based on Vision-Language Models (VLMs), such as CLIP, which can adaptively adjust weights for classifiers with text features to mitigate overfitting. Specifically, FGPL first motivates the prompts to learn content and domain semantic information by capturing Domain-Agnostic and Domain-Specific features. Furthermore, our prompts are designed to be category-generalized by diversifying the Domain-Specific prompts. Additionally, we design an Adaptive Convolutional Adapter (AC-adapter), which is implemented through an adaptive combination of Vanilla Convolution and Central Difference Convolution, to be inserted into the image encoder for quickly bridging the gap between general image recognition and FAS task. Extensive experiments demonstrate that the proposed FGPL is effective and outperforms state-of-the-art methods on several cross-domain datasets. © 2024 ACM.

关键词： Semantics

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