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Complete weight enumerators of few-weight linear codes

Cryptography and Communications 2025年 1-38页

作者： Zeng, Xiangdi Meng, Xiangrui Gao, Jian Fu, Fang-Wei School of Mathematics and Statistics Shandong University of Technology Zibo China Chern Institute of Mathematics and LPMC Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

Few-weight linear codes have important applications in the construction of strongly regular graphs, authentication codes and secret sharing schemes. In this paper, some few-weight linear codes are constructed from proper defining sets over finite fields. Their complete weight enumerators are explicitly determined using Weil sums. As applications, we give two classes of new projective three-weight linear codes, which achieve the Griesmer bound. We construct some new strongly regular graphs and infinite families of minimal three-weight linear codes with $$\frac{w_{min}}{w_{max}}\le \frac{p-1}{p}$$ . Moreover, some new authentication codes are presented. Our results generalize and improve the work of Zhu and Liao (Finite Fields Appl. 75, 101897, 2021).

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Adversarial training via multi-guidance and historical memory enhancement

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Neurocomputing 2025年 619卷

作者： Zhao, Chenyu Qian, Yaguan Wang, Bin Gu, Zhaoquan Ji, Shouling Wang, Wei Zhang, Yanchun School of Science Zhejiang University of Science and Technology China Network and Data Security China China School of Computer Science and Technology Zhejiang University China Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University China Institute for Sustainable Industries and Liveable Cities Victoria University Australia

Deep neural networks (DNNs) are often susceptible to the influence of adversarial examples, potentially leading to severe security issues. Adversarial training stands out as one of the most effective defenses. In this paper, we empirically demonstrate that weakly robust models maintain resilience to adversarial examples generated on a fully trained robust model. Building on this observation, we introduce a novel weight ensemble training method named Multi-Guided Adversarial Training (MGAT). MGAT improves both clean and adversarial accuracy through two guiding strategies: self-guidance and expert-guidance. Self-guidance promotes diversity in decision boundaries among member models, while expert-guidance aligns them more closely with natural decision boundaries. Additionally, MGAT enhances the historical memory of model weights using an exponential moving average (EMA) with a memory factor, thereby better memorizing the weights of weakly robust models. Our experiments demonstrate that MGAT is highly effective in defending against a range of adversarial attacks. Notably, MGAT achieves an accuracy of 55.48% against AutoAttack, marking a 10.92% improvement over standard adversarial training on CIFAR-10. © 2024 Elsevier B.V.

关键词： Generative adversarial networks

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Nonlinear Interference-Aware Routing,Wavelength and Power Allocation in C+L+S Multi-Band Optical networks

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China Communications 2025年第4期22卷 129-142页

作者： Zhang Xu Xie Wang Feng Chuan Zeng Hankun Zhou Shanshan Zhang Fan Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China

Multi-band optical networks are a potential technology for increasing network ***,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical *** overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength ***,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical *** objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not ***,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation ***,we conduct simulations under different test *** simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.

关键词： multiband optical communications multiband optical networks power allocation wavelength assignment

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CPSNet: Comprehensive Enhancement Representation for Polyp Segmentation Task

CPSNet: Comprehensive Enhancement Representation for Polyp S...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cai, Jiati Liu, Xiaogang Yang, Hongjie Ding, Yi Zhong, Ting Qin, Zhen Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Department of Gastroenterology and Hepatology University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and shape variations among polyps, their extreme similarity to the background, and frequent rotation of the lens, which further increases the diversity in polyp presentation. To address these challenges effectively, we propose a comprehensive polyp segmentation network (CPSNet). Specifically, we introduce a Comprehensive Spatial Feature Extraction Module (CFEM) that progressively and densely integrates features while forming receptive windows with various shapes. This enables enhanced perception of polyps at manifold sizes and shapes. Additionally, we propose a Fine-grained Region Strengthen Module (FGSM) to supplement uncertain areas around polyps by mitigating background noise interference. In terms of training strategy, we further introduce a Rotation-augmented Constrained Loss (RC Loss), which reinforces consistency constraints on polyp images under multiple rotation angles. Qualitative and quantitative experiments conducted on five public datasets demonstrate both the plug-and-play capability of CFEM as well as the effectiveness and excellence achieved by CPSNet. © 2025 IEEE.

关键词： colonoscopy computer vision polyp segmentation representation enhancement

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Practical Two-Factor Authentication Protocol for Real-Time data Access in WSNs

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Xu, Meijia Wang, Ding Nankai University College of Cryptology and Cyber Science Tianjin300350 China Tianjin300350 China Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China

With the rapid development of sensors and communication technologies, the Internet of Things (IoT) paradigm has increasingly expanded to cover various daily, industrial, and military applications. As a core component of the IoT responsible for environmental sensing and data collection, wireless sensor networks (WSNs) face significant security threats. The design of multi-factor authentication schemes to secure real-time data transmission in WSNs has garnered considerable research efforts. However, a common trend in existing multi-factor authentication protocols is emphasizing performance and security benefits, while possible limitations are rarely subjected to thorough analysis. To fill this gap, we first select two representative multi-factor authentication schemes (i.e., Chaudhry et al.'s scheme at ACM ToIT'21 and Jabbari-Mohasef's scheme at IEEE TII'22) as case studies, and point out that both schemes are vulnerable to offline password guessing and node capture attacks, and fail to achieve forward secrecy. We then investigate the fundamental causes of these weaknesses underlying the schemes and propose corresponding solutions. After rethinking previous schemes, we present a new robust two-factor authentication scheme for WSNs and formally prove its security under the Random Oracle Model. Furthermore, we compare our scheme with 18 state-of-the-art protocols using the widely accepted evaluation framework. The comparison results show that our protocol outperforms its foremost counterparts. © 2004-2012 IEEE.

关键词： Wireless sensor networks

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FedFLD: Heterogeneous Federated Learning via Forget-Less Distillation

FedFLD: Heterogeneous Federated Learning via Forget-Less Dis...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoyang Yi Jian Zhang Jing Chen Yuru Bao Lingkai Xing College of Cyber Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated learning, as a distributed machine learning paradigm, enhances privacy protection but faces the challenge of heterogeneity. data-free knowledge distillation (DFKD) methods attempt to overcome this challenge by using a generator to synthesize samples for fine-tuning a global model. However, these methods often suffer from significant shifts in output distribution, leading to catastrophic forgetting. To tackle these issues, we propose FedFLD, a novel federated forget-less distillation framework that mitigates catastrophic forgetting in DFKD while addressing the heterogeneity challenge. Specifically, FedFLD guides the generator’s training from three key aspects and constrains the output distribution with an elastic weight consolidation penalty term. By synthesizing diverse samples from different perspectives through additional generator updates, FedFLD facilitates effective knowledge distillation from local models to the global model. Additionally, the global model is further optimized via the heterogeneity fine-tuning process, mitigating the bias from heterogeneity and resulting in a more expressive and robust model.

关键词： Training Privacy Federated learning Signal processing Generators Acoustics Servers Speech processing Protection Faces

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OSE: On-Site State of Health Estimation for Li-ion Battery using Real-Time Field data

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IEEE Transactions on Transportation Electrification 2025年

作者： Chen, Yanwen Zhao, Cong Yang, Shusen Zhao, Peng Ren, Xuebin Han, Qing Yang, Yuqian Wu, Shuaijun Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Shaanxi Xi'an710049 China Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics The Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi'an710049 China

On-site lithium-ion battery state of health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low-quality of unlabeled real-time field data, diverse operating environments of in-service EVs, and limited computational capability of onboard devices, existing techniques established on data from well-controlled experimental environments are not practical for real world EVs' SoH estimation. Accurate and rapid SoH estimation based on field data of in-service EVs still remains quite challenging. To tackle this challenge, we present an on-site SoH estimation (OSE) method using in-service EV field data through a new knowledge embedded deep transfer learning (DTL) model. Initially, an universal data preprocessing approach integrating mechanism knowledge is designed to process low-quality data under diverse operating environments. Then, we develop a domain adaptive hybrid deep neural network (DAHDNN) model suitable for unlabeled field data, which can be deployed via edge cloud collaborative framework to meet actual computational capability. We demonstrate the superiority of our method across four real datasets, where, OSE's estimation error is decreased by up to 78.5% compared with the state-of-the-art methods. The results indicate that the proposed method has good generalizability and reliability for SoH estimation on real-time field data. © 2015 IEEE.

关键词： State of charge

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MMEFU-Net: A Mamba-Guided Multi-Encoder Fusion U-Net for Tumor Segmentation in CT Images

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IEEE Access 2025年 13卷 76257-76270页

作者： Xue, Renzheng Zhang, Zifeng Liang, Minghui Zhao, Yaxin Zhang, Qing Qiqihar University School of Computer and Control Engineering 161000 China Qiqihar University Key Laboratory of Big Data Network Security Detection and Analysis Heilongjiang Province China Qiqihar Medical University School of Medical Technology 161000 China

Tumor segmentation is crucial for cancer diagnosis, treatment planning, and surgical interventions. However, traditional manual delineation methods are time-consuming, labor-intensive, and prone to subjective variability, highlighting the need for automated approaches. The significant variability in lesion shape and size within medical images further complicates segmentation tasks, necessitating models that can effectively capture both local and global features to achieve accurate results. To address these challenges, we propose MMEFU-Net, a novel deep learning framework designed for efficient and accurate tumor segmentation in medical CT images. The architecture integrates a multi-encoder structure comprising a detail branch, a context branch, and a Mamba-guided branch to extract complementary features, addressing the limitations of single-encoder models. Key innovations include the Mamba-Guided Fusion Attention (MGFA) module for balancing local details and global context, the Pixel Attention Feature Fusion (PAFF) module for enhancing detail branch features with complementary information from the context branch, the Dilated Multi-Scale Fusion (DMSF) module for robust multi-scale feature modeling, and the introduction of the lightweight DySample operator for precise and efficient dynamic upsampling. Comprehensive experiments on five public and private benchmark datasets, encompassing both liver tumors and lung cancers, demonstrate that MMEFU-Net outperforms state-of-the-art U-Net, Transformer-based, and Mamba-based architectures. The model achieves superior Dice Similarity Coefficients (DSC) and Intersection over Union (IoU) scores while significantly reducing computational costs. Notably, MMEFU-Net improves the DSC by 2.16% compared to nnU-Net on the LiTS2017 dataset, with a 35× reduction in parameters and a 25× reduction in computational complexity. These findings suggest that MMEFU-Net has the potential to become a scalable and efficient solution for tumor segmen

关键词： Image segmentation

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SRv6-INT Enabled network Monitoring and Measurement: Toward High-Yield network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

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AdaptPUD: An accurate URL-based detection approach against tailored deceptive phishing websites

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Computer networks 2025年 265卷

作者： Zhang, Ziliang Wu, Jinmin Lu, Ning Shi, Wenbo Liu, Zhiquan School of Computer Science and Engineering Northeastern University Shenyang China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University at Qinhuangdao Qinhuangdao China College of Cyber Security Jinan University Guangzhou China

It is well-known that Uniform Resource Locator (URL)-based detection is essential for the swift initial screening of phishing websites. However, when facing tailored deceptive websites, a new variant of phishing attacks with imitative and dynamic features, existing works may be inaccurate due to the lack of adequate data representation and appropriate detection models. For this, we propose an accurate URL-based detection approach against tailored deceptive websites, termed as AdaptPUD. It first devises the token-property embedding method to extract more representations from URL data. And then, design a multi-channel detection model integrated with the concept drift, which can support dynamically incremental learning while maintaining high accuracy. We conduct performance evaluations and practical applications to demonstrate the superiority of AdaptPUD. The results indicate that its accuracy achieves 91.22% on the latest concept drift dataset, outperforming the baselines by 6.20% to 42.93%. © 2025 Elsevier B.V.

关键词： network security

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