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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

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Frontiers of computer Science 2024年第6期18卷 215-227页

作者： Xiaochuan LIN Kaimin WEI Zhetao LI Jinpeng CHEN Tingrui PEI College of Information Science and Technology&Cyberspace Security Jinan UniversityGuangzhou 510632China National&Local Joint Engineering Research Center of Network Security Detection and Protection Technology Guangzhou 510632China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Spatial crowdsourcing(SC)is a popular data collection paradigm for numerous *** the increment of tasks and workers in SC,heterogeneity becomes an unavoidable difficulty in task *** researches only focus on the single-heterogeneous task ***,a variety of heterogeneous objects coexist in real-world SC *** dramatically expands the space for searching the optimal task allocation solution,affecting the quality and efficiency of data *** this paper,an aggregation-based dual heterogeneous task allocation algorithm is put *** investigates the impact of dual heterogeneous on the task allocation problem and seeks to maximize the quality of task completion and minimize the average travel *** problem is first proved to be ***,a task aggregation method based on locations and requirements is built to reduce task ***,a time-constrained shortest path planning is also developed to shorten the travel distance in a *** that,two evolutionary task allocation schemes are ***,extensive experiments are conducted based on real-world datasets in various *** with baseline algorithms,our proposed schemes enhance the quality of task completion by up to 25% and utilize 34% less average travel distance.

关键词： task allocation aggregation shortest path dual heterogeneous spatial crowdsourcing

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Towards real-time ML-based DDoS detection via cost-efficient window-based feature extraction

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Science China(information Sciences) 2023年第5期66卷 114-129页

作者： Haibin LI Yi ZHAO Wenbing YAO Ke XU Qi LI Department of Computer Science and Technology Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Institute for Network Sciences and Cyberspace Tsinghua University

Distributed denial of service(DDoS) detection is still an open and challenging problem. In particular, sophisticated attacks, e.g., attacks that disguise attack packets as benign traffic always appear, which can easily evade traditional signature-based methods. Due to the low requirements for computing resources compared to deep learning, many machine learning(ML)-based methods have been realistically deployed to address this issue. However, most existing ML-based DDo S detection methods are highly dependent on the features extracted from each flow, which incur remarkable detection delay and computation overhead. This article investigates the limitations of typical ML-based DDo S detection methods caused by the extraction of flow-level features. Moreover, we develop a cost-efficient window-based method that extracts features from a fixed number of packets periodically, instead of per flow, aiming to reduce the detection delay and computation overhead. The newly proposed window-based method has the advantages of well-controlled overhead and wide support of common routers due to its simplicity and high efficiency by design. Through extensive experiments on real datasets, we evaluate the performance of flow-based and window-based *** experimental results demonstrate that our proposed window-based method can significantly reduce the detection delay and computation overhead while ensuring detection accuracy.

关键词： DDoS attack machine learning feature extraction detection delay cost-efficiency

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Enhancing Malware Detection Resilience:A U-Net GAN Denoising Framework for Image-Based Classification

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computers, Materials & Continua 2025年第3期82卷 4263-4285页

作者： Huiyao Dong Igor Kotenko Faculty of Information Technology and Security ITMO National Research UniversitySt.Petersburg197101Russia Laboratory of Computer Security Problems St.Petersburg Federal Research Center of the Russian Academy of SciencesSt.Petersburg199178Russia

The growing complexity of cyber threats requires innovative machine learning techniques,and image-based malware classification opens up new ***,existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection,and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these *** study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware *** proposed methodology addresses existing classification limitations by introducing noise addition,which simulates obfuscated malware,and denoising strategies to restore robust image *** evaluate the approach,we used multiple CNN-based classifiers to assess noise resistance across architectures and datasets,measuring significant performance *** denoising technique demonstrates remarkable performance improvements across two multi-class public datasets,MALIMG and *** example,the MALIMG classification accuracy improved from 23.73%to 88.84%with denoising applied after Gaussian noise injection,demonstrating *** approach contributes to improving malware detection by offering a robust framework for noise-resilient classification in noisy conditions.

关键词： Malware cybersecurity deep learning denoising

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A Fault-Tolerant Group Key Management Scheme for the Internet of Things Based on Multi-Layer Blockchain

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IEEE Internet of Things Journal 2025年第11期12卷 18004-18018页

作者： Hou, Zhiwen Pei, Tingrui Li, Ming Wei, Kaimin Chen, Yingyang Cao, Sixing Jinan University College of Cyber Security Guangzhou510632 China Jinan University The Natl. Joint Engineering Research Center of Network Security Detection and Protection Technology College of Information Science and Technology Jinan University College of Cyber Security Guangzhou510632 China Jinan University College of Information Science and Technology Guangzhou510632 China

The importance of group communication in the context of the Internet of Things (IoT) is growing, yet the security and stability of this communication are facing significant challenges. The prevailing distributed group key management schemes are ill-suited to resource-constrained devices. Furthermore, those that rely on servers are vulnerable to single-point failures and Byzantine risks. The distributed, immutable, and automatic execution of smart contracts on blockchains may offer a potential solution to these problems. This paper puts forth a multi-layer blockchain-based IoT group key management scheme with Byzantine fault tolerance. The scheme oversees the management of IoT device subgroups through the deployment of blockchain and smart contracts on edge servers while overseeing the entire device group in a hierarchical structure. A redundant selection mechanism based on hash mapping has been designed to guarantee reliable communication between disparate blockchains and devices. Concurrently, the scheme incorporates a server detection mechanism for Byzantine behavior, thereby ensuring the stability of the blockchain. The results of the experimental analysis demonstrate that the scheme exhibits enhanced security and fault tolerance. © 2014 IEEE.

关键词： Blockchains Servers Internet of Things security Fault tolerant systems Fault tolerance Smart contracts Protocols Scalability Electronic mail

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A Blockchain-Based Efficient Cross-Domain Authentication Scheme for Internet of Vehicles

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computers, Materials & Continua 2024年第7期80卷 567-585页

作者： Feng Zhao Hongtao Ding Chunhai Li Zhaoyu Su Guoling Liang Changsong Yang Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China

The Internet of Vehicles(IoV)is extensively deployed in outdoor and open environments to effectively address traffic efficiency and safety issues by connecting vehicles to the ***,due to the open and variable nature of its network topology,vehicles frequently engage in cross-domain *** such processes,directly uploading sensitive information to roadside units for interaction may expose it to malicious tampering or interception by attackers,thus compromising the security of the cross-domain authentication ***,IoV imposes high real-time requirements,and existing cross-domain authentication schemes for IoV often encounter efficiency *** mitigate these challenges,we propose CAIoV,a blockchain-based efficient cross-domain authentication scheme for *** scheme comprehensively integrates technologies such as zero-knowledge proofs,smart contracts,and Merkle hash tree *** divides the cross-domain process into anonymous cross-domain authentication and safe cross-domain authentication phases to ensure efficiency while maintaining a balance between efficiency and ***,we evaluate the performance of *** results demonstrate that our proposed scheme reduces computational overhead by approximately 20%,communication overhead by around 10%,and storage overhead by nearly 30%.

关键词： Blockchain cross-domain authentication internet of vehicle zero-knowledge proof

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Automated federated learning‐based adversarial attack and defence in industrial control systems

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IET Cyber-Systems and Robotics 2024年第2期6卷 109-127页

作者： Guo-Qiang Zeng Jun-Min Shao Kang-Di Lu Guang-Gang Geng Jian Weng College of Cyber Security Jinan UniversityGuangzhouChina National Joint Engineering Research Center of Network Security Detection and Protection Technology Jinan UniversityGuangzhouChina College of Information Science and Technology Donghua UniversityShanghaiChina

With the development of deep learning and federated learning(FL),federated intrusion detection systems(IDSs)based on deep learning have played a significant role in securing industrial control systems(ICSs).However,adversarial attacks on ICSs may compromise the ability of deep learning-based IDSs to accurately detect cyberattacks,leading to serious ***,in the process of generating adversarial samples,the selection of replacement models lacks an effective method,which may not fully expose the vulnerabilities of the *** authors first propose an automated FL-based method to generate adversarial samples in ICSs,called AFL-GAS,which uses the prin-ciple of transfer attack and fully considers the importance of replacement models during the process of adversarial sample *** the proposed AFL-GAS method,a lightweight neural architecture search method is developed to find the optimised replacement model composed of a combination of four lightweight basic ***,to enhance the adversarial robustness,the authors propose a multi-objective neural archi-tecture search-based IDS method against adversarial attacks in ICSs,called MoNAS-IDSAA,by considering both classification performance on regular samples and adver-sarial robustness *** experimental results on three widely used intrusion detection datasets in ICSs,such as secure water treatment(SWaT),Water Distribution,and Power System Attack,demonstrate that the proposed AFL-GAS method has obvious advantages in evasion rate and lightweight compared with other four ***,the proposed MoNAS-IDSAA method not only has a better classification performance,but also has obvious advantages in model adversarial robustness compared with one manually designed federated adversarial learning-based IDS method.

关键词： adversarial attacks adversarial robustness automated federated learning Industrial control system intrusion

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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Decoupled knowledge distillation method based on meta-learning

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High-Confidence Computing 2024年第1期4卷 1-6页

作者： Wenqing Du Liting Geng Jianxiong Liu Zhigang Zhao Chunxiao Wang Jidong Huo Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250014China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250014China Aerospace Science&Industry Network Information Development Co. LTDBeijing 100854China

With the advancement of deep learning techniques,the number of model parameters has been increasing,leading to significant memory consumption and limits in the deployment of such models in real-time *** reduce the number of model parameters and enhance the generalization capability of neural networks,we propose a method called Decoupled MetaDistil,which involves decoupled *** method utilizes meta-learning to guide the teacher model and dynamically adjusts the knowledge transfer strategy based on feedback from the student model,thereby improving the generalization ***,we introduce a decoupled loss method to explicitly transfer positive sample knowledge and explore the potential of negative samples *** experiments demonstrate the effectiveness of our method.

关键词： Model compression Knowledge distillation Meta-learning Decoupled loss

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The Study of Multi-type Computing Power Trading Mechanism in Computing Power network Based on Blockchain and Combinatorial Double Auction 24

The Study of Multi-type Computing Power Trading Mechanism in...

引用

4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Zhao, Yue Wang, Jizhi Kong, Lingrui Sui, Tongtong Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Provincial Key Laboratory of Industrial Network and Information System Security Qilu University of Technology Shandong Academy of Sciences Jinan Shandong China Quancheng Laboratory Jinan Key Laboratory of Digital Security Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (纸本)9798400710247

The advancement of 5G and mobile internet technologies has propelled the development of emerging businesses and applications, demanding higher requirements for network bandwidth and computational resources. To address this challenge, the computing power network (CPN) has emerged. The CPN integrates computing and networking in depth, achieving collaborative allocation of distributed computing resources, enhancing the efficient utilization of computing resources, and improving user experiences in network services. However, in the development of fields such as Extended Reality (XR), Smart Healthcare, Connected Vehicles, and Artificial Intelligence (AI), there is a multidimensional, complex, and rapidly growing demand for computing resources. Therefore, it is crucial to establish a trading mechanism that integrates multiple computing resources in CPN. In this paper, a computing power trading scheme based on blockchain and combinatorial double auction is proposed. Firstly, a computing power trading system based on blockchain was designed, leveraging the decentralized and highly secure characteristics of blockchain technology to enhance the security of computing power transactions. Furthermore, a multi-type computing power trading mechanism (MCPT) based on blockchain and combinatorial double auction was introduced. The use of combinatorial double auctions as an incentive mechanism enables the trading of multiple computing power resources and results in high social welfare. Moreover, a discrete rime optimization algorithm (DRIME) is designed in the proposed mechanism (MCPT) to better solve the computing trading problem. Through simulation experiments, the effectiveness of the approach is validated. © 2024 Copyright held by the owner/author(s).

关键词： Computing power

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