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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Ming Northeastern University School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

Mobile edge computing (MEC) integrated with network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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A study of watermarking techniques for data publishing

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Multimedia Tools and Applications 2025年 1-30页

作者： Yuan, Shuguang Yu, Jing Yang, Ke Wang, Yuyang Li, Zhaochen Qiu, Jiabao Yang, Tengfei Chen, Chi Institute of Information Engineering Chinese Academy of Science Beijing China State Key Laboratory of Cyberspace Security Defense Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

In today’s digital era, data sharing is becoming an emerging trend. However, copying, manipulating, and distributing multimedia data over an open channel is easy. Watermarking is one of the most promising methods for copyright protection, tamper detection, and traitor tracing tasks. In the past decades, many diversified watermarking techniques have been created and are still developing, which entails various requirements and challenges. In this paper, we survey recent watermarking techniques on data publishing scenarios. The main contributions of this paper include: 1) We explain the classification methods and background knowledge of watermarking techniques, such as types of function, watermarking process, and basic concepts;2) We primarily survey watermarking techniques for data publishing on different carriers, i.e., relational database, text, print and social media;3) We give representation figures and detailed analysis tables for the essential researches;4) Finally, it provides a discussion of future work according to analysis and observation of existing works. This survey allows researchers, users, and engineers to use efficient watermarking tools to secure published data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Watermarking

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Quasi-Linearly Homomorphic Signature for Data Integrity Auditing in Cloud Storage 20th

Quasi-Linearly Homomorphic Signature for Data Integrity Aud...

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20th International Conference on information security and Cryptology, Inscrypt 2024

作者： Zhang, Futai Huang, Yichi Yang, Wenjie Tian, Jinmei Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou 350117 China

ISBN: (纸本)9789819647330

As the popularity of cloud storage increases, data security issues have attracted significant attention from both academia and business sectors. Among these issues, data integrity auditing is of particular importance. To date, numerous data integrity auditing protocols for cloud storage have been available in the literature. Most of them are based on public key cryptography. In this paper, we examine the key technique involved in public key data integrity auditing in cloud storage. The concept and the security model of quasi-linearly homomorphic signature(QLHS) scheme are introduced. Its relationship with secure public key data integrity auditing in cloud storage is demonstrated. An efficient instantiation of the quasi-linearly homomorphic signature scheme is presented. The performance of a provable data possession(PDP) protocol using our QLHS scheme is analyzed. The comparison demonstrates that our approach is more efficient in terms of computation, communication, and storage space for cloud users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： bilinear map cloud storage data integrity auditing provable data possession

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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A circuit area optimization of MK-3 S-box

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Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

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CNSD-Par: container network isolation scheme based on behavioral similarity

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Journal of Supercomputing 2025年第8期81卷 1-25页

作者： He, Xinfeng Miao, Ruifeng School of Cyber Security and Computer Hebei University Baoding 071000 China Hebei Key Laboratory of High Trusted Information System Hebei University Baoding 071000 China

With the rapid development of containerization technology, the workload of container safety isolation management gradually increased, but the efficiency was not high. At the same time, safety problems caused by insufficient container isolation capabilities became increasingly apparent. We proposed a container network isolation scheme based on behavior similarity in this context. First, the system call sequence was used as the benchmark for measuring the behavior similarity of containers, and an optimized hierarchical clustering algorithm (N-HCluster) was employed to divide containers with similar behavior into the same container network security domain. We then designed a container network isolation architecture based on security domains to enhance the container network’s isolation and security. Finally, we evaluated this protocol’s feasibility, accuracy, and performance through experiments. The experimental results showed that this scheme was effective and feasible and demonstrated good container network isolation capabilities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Container behavior similarity Container network isolation Container security Hierarchical clustering algorithm System call sequence

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Density Peak Clustering Algorithm Based on Data Field Theory and Grid Similarity

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Journal of computer Science and Technology 2025年第2期40卷 301-321页

作者： Yu, Qing-Ying Shi, Ge-Ge Xu, Dong-Sheng Wang, Wen-Kai Chen, Chuan-Ming Luo, Yong-Long School of Computer and Information Anhui Normal University Wuhu241002 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu241002 China

The density peak clustering algorithm can rapidly identify cluster centers by drawing decision graphs without any prior knowledge;however, when multiple density peaks are present in one cluster of the dataset, the cluster centers cannot be accurately obtained, leading to incorrect clustering. Moreover, the single-step allocation strategy is poorly fault-tolerant and can lead to successive allocation errors. To address these problems, this study proposes a density peak clustering method based on data field theory and grid similarity, which divides the original data into grid spaces to obtain subspace codes and grid data. Subsequently, the potential grid density is introduced to measure the density of each grid based on the data field, and the density peaks are identified to obtain the cluster centers from the high-density grids. Finally, self-organized clustering is realized based on neighborhood extension centers and grid similarity. It thus reduces the possibility of successive assignment errors and the negative impact of multiple density peaks on the clustering results. In addition, the algorithm automatically determines the cluster centers and can correctly assign the non-density-peak grid to the corresponding clusters. Experimental results with synthetic and real-world datasets demonstrate that the proposed algorithm outperforms similar algorithms in terms of accuracy and efficiency when dealing with complex datasets with large density differences and cross-tangling. © Institute of Computing Technology, Chinese Academy of Sciences 2025.

关键词： Clustering algorithms

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Enhancing Parameter-Efficient Transformers with Contrastive Syntax and Regularized Dropout for Neural Machine Translation 21st

Enhancing Parameter-Efficient Transformers with Contrastive...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Fan, Yunlong Yang, Zhiheng Li, Baixuan Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9789819601219

Transformers have significantly improved Neural Machine Translation (NMT) models, accompanied by the inherent space complexity of O(n2). While recent approaches aim to be parameter-efficient, they often exhibit limited generalization capabilities with fewer parameters and decreased performances in longer sentences. To this end, we propose two methods, Syntax-enhanced Contrastive Learning (Syn-CL) and JS divergence-based Regularized Dropout (JSR-Drop) for the cross-layer parameter-sharing Transformer (baseline) in NMT. In Syn-CL, we add the corresponding target-to-source instances for the same batch in an extra bidirectional training phase, and minimize the distances of syntax-enhanced representation between the bilingual sentence-pair while maximizing the ones among others. In JSR-Drop, we refine the regularized dropout strategy with JS divergence, and improve generalization capabilities by reducing Transformers’ inconsistencies between training and inference introduced by dropout. Extensive experiments on six NMT tasks, IWSLT2014 (German↔English) and IWSLT2017 (English↔French and English↔Romanian), show an average BLEU score of 37.55, surpassing the vanilla Transformer and the baseline up to 1.75 BLEU, and outperforming models with 1.5×, 2.0× parameters in translating long sentences. Our code is available at https://***/Anon1214/EnPeNMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Random Staircase Generator Matrix Codes: Coding Theorem, Performance Analysis, and Code Design

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IEEE Transactions on information Theory 2025年第5期71卷 3497-3509页

作者： Wang, Qianfan Wang, Yiwen Wang, Yixin Liang, Jifan Ma, Xiao Sun Yat-sen University School of Computer Science Engineering and Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China Sun Yat-sen University School of Systems Science and Engineering Guangzhou510006 China

In this paper, we present a class of codes, referred to as random staircase generator matrix codes (SGMCs), which have staircase-like generator matrices. In the infinite-length region, we prove that the random SGMC is capacity-achieving over binary-input output-symmetric (BIOS) channels. In the finite-length region, we propose the generalized representative ordered statistics decoding with local constraints (LC-ROSD) algorithm for the SGMCs. The most distinguished feature of the SGMCs with LC-ROSD is that the staircase-like matrices enable parallel implementation of the Gaussian elimination (GE), avoiding the serial GE of conventional OSD and supporting a potential low decoding latency, as implied from simulations. To analyze the performance of random SGMCs in the finite-length region, we derive the ensemble weight spectrum and invoke the conventional union bound. We also derive a partially random coding union (RCU) bound, which is tighter than the conventional one and is used as a criterion to design the SGMCs. Staircase-like generator matrices allow us to derive a series of (tighter and tighter) lower bounds based on the second-order Bonferroni inequality with the incremental number of codewords. The numerical results show that the decoding performance can match well with the proposed partially RCU bound for different code rates and different profiles. The numerical results also show that the tailored SGMCs with the LC-ROSD algorithm can approach the finite-length performance bound, outperforming the 5G low-density parity-check (LDPC) codes, 5G polar codes, and Reed-Muller (RM) codes. © 1963-2012 IEEE.

关键词： Decoding

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

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

作者： Li, Guoliang Zhang, Ruiqi Sun, Zhe Xing, Lingkai Zhang, Yu College of Cyber Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin China

ISBN: (纸本)9798350368741

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset's quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR. © 2025 IEEE.

关键词： deep learning distribution estimation dynamic relabeling malicious traffic detection noisy long-tailed problem

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