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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yao, Honglei Liu, Donglan Chang, Yingxian Liu, Xin Tang, Chaofan State Grid Shandong Electric Power Research Institute Jinan China State Grid Shandong Electric Power Company Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Time series anomaly detection often faces significant challenges due to the high dimensionality, interdependence, and label sparsity of the data. Traditional methods struggle to model the intricate relationships between multiple time series components and often fail to accurately detect anomalies due to insufficient data dependence mining. To address these issues, we introduce GCRNF, a novel Graph-Enhanced Standardized Flow Model that incorporates a Bayesian network for causality modeling. This model decomposes joint probabilities into conditional probabilities and utilizes a graph dependency encoder with hybrid CNN-RNN models and Graph Convolutional networks (GCNs). Our experimental results demonstrate that GCRNF outperforms existing methods in density estimation, anomaly detection, and drift identification within time series distributions. Specifically, GCRNF showed a significant improvement in detection accuracy and robustness compared to traditional methods, providing a more effective solution for identifying anomalies in complex multivariate time series data. © 2024 IEEE.

关键词： Markov processes

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A Triangular Stable Node network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang 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 Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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EVPIR: Efficient and Verifiable Privacy-Preserving Image Retrieval in Cloud-assisted Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Li, Mingyue Li, Yuntao Du, Ruizhong Jia, Chunfu Shao, Wei Hebei University School of Cyber Security and Computer Baoding071000 China Key Lab on High Trusted Information Sys tem in Hebei Province Hebei China College of Cyber Sciences Nankai university Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China Jinan250014 China

With the proliferation of mobile devices and the advancement of cloud computing capabilities, cloud-assisted Internet of Things (IoT) attracts increased attention based on its computational and storage advantages. Upon these conveniences, there also raises privacy concerns that numerous solutions have been proposed to solve it. However, existing methods suffer from challenges such as low retrieval accuracy, inefficiency in large-scale image retrieval, and lack of efficient result verification. In this paper, we propose an Efficient Verifiable Privacy-preserving Image Retrieval scheme (EVPIR). Specifically, we design a hierarchical graph index to significantly enhance retrieval efficiency, which organizes image feature vectors into a multi-level structure, establishing connections between neighboring nodes within each layer and creating a highly structured and efficient retrieval framework. During the retrieval process, we employ greedy search algorithm to navigate these connections and identify the closest neighbors across different levels, which make the proposed multi-level approach reduce the search space at each level, achieving faster and more accurate retrieval. Furthermore, we design an efficient dynamic verifiable framework leveraging Chameleon hash functions and BLS signatures where we utilize Chameleon hash nodes based on Merkle Hash Trees (MHT) to enable dynamic updates of the verification tree and employ BLS signatures to construct multiple verification nodes for effectively shortening the verification path. Finally, security analysis shows that EVPIR can defend various threat models and extensive experiments further demonstrates that EVPIR can improve retrieval and verification efficiency. (Figure presented) © 2014 IEEE.

关键词： Differential privacy

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Discrete Jaya algorithm to solve the police resource deployment problem in emergency traffic accident 10

Discrete Jaya algorithm to solve the police resource deploym...

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10th IEEE Joint International information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Jing, Zhang Weijie, Ding Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Aiming at the high frequency of urban traffic accidents and the relative shortage of police resources, a multi-objective discrete Jaya algorithm is proposed to solve the problem of police resource deployment in emergency traffic accident handling. The purpose is to make timely response and handling of traffic accidents through the rational allocation of police deployment points in the jurisdiction. Taking the coverage of accident points and the average response time of accidents as the optimization objectives, the discrete Jaya algorithm is used as the global search strategy to approach the Pareto front by constantly approaching the optimal solution and far away from the worst solution, and then the neighborhood search strategy is used to improve the local search ability of the algorithm. Finally, a practical example is given to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Law enforcement

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Multi-scale Community Detection in Subspace of Attribute 28th

Multi-scale Community Detection in Subspace of Attribute

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yan, Cairui Ma, Huifang Tang, Yuechen Li, Xiaohong Li, Zhixin College of Computer Science and Engineering Northwest Normal University Gansu Lanzhou730070 China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guangxi Guilin541004 China

ISBN: (纸本)9783031306747

The goal of attributed community detection is to search a partition of the network such that there is high cohesion within each group and low coupling between two groups. We argue that multiple partitions of the attributed network should be captured with different semantics and community detection should be approached from the perspective of attribute subspace. In this paper, we integrate spectral wavelets with attribute subspace, and develop a framework of Multi-scale Community Detection in Subspace of Attribute (MCDSA). Our idea is to implement graph partitioning via scale-dependent modularity and independent attribute subspaces, thus making our model more flexible and effective. In MCDSA, communities at each scale have independent attribute subspace, which is helpful to analyze the importance of each attribute under different network partition, better revealing the relationship between nodes. Extensive experiments on multiple benchmark datasets show that, the quality of community detection can be remarkably enhanced under the regime of attribute subspaces, achieving the state-of-the-art performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Early Detection of Autism in Children Using Transfer Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 11-22页

作者： Taher M.Ghazal Sundus Munir Sagheer Abbas Atifa Athar Hamza Alrababah Muhammad Adnan Khan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Lahore Garrison University Lahore54000Pakistan Department of Computer Science COMSATS University Islamabad–Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnamGyeonggido13120Korea

Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking *** early detection of ASD is essential for delivering effective,timely *** facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early *** study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)*** model achieved a detection accuracy of 87.7%and performed better than other established ASD detection ***,this model could facilitate the early detection of ASD in clinical practice.

关键词： Autism spectrum disorder convolutional neural network loss rate transfer learning AlexNet deep learning

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Reputation based Adaptive Federated Learning in Medical Scenarios

Reputation based Adaptive Federated Learning in Medical Scen...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Lianhai Wang Tianrui Liu Yingxiaochun Wang Xinlei Wang Qi Li 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) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated Learning (FL) is a distributed machine learning paradigm designed to address data silos and protect data privacy. However, in medical scenarios, the heterogeneity in data quality and the non-independent and identically distributed (Non-IID) nature of medical data from different hospitals can lead to degraded model performance in FL. To tackle this challenge, this paper proposes an adaptive federated learning framework based on reputation. By using a reputation evaluation model, the framework adaptively adjusts the aggregation weights of the models to enhance the performance of the global model. Additionally, our reputation evaluation model, integrated with blockchain technology, can rapidly identify malicious clients and effectively prevent poisoning attacks. Experimental results on two medical datasets demonstrate that this framework achieves higher model accuracy and faster convergence.

关键词： Adaptation models Data privacy Federated learning Hospitals Data integrity Computational modeling Evaluation models Data models security Convergence

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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LCLB: Low Cross-Chain Transaction Ratio and Load Balancing for Blockchain Sharding

LCLB: Low Cross-Chain Transaction Ratio and Load Balancing f...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Yu Wang Xiaoming Wu Qile Yuan Yunfeng Dong Xiangzhi Liu 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) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

As blockchain technology becomes widely applied, handling high volumes of transactions has become a critical challenge. Sharding has emerged as an effective solution to enhance the throughput of blockchain systems. Yet, this approach introduces challenges related to the frequency of cross-shard transactions and workload balancing. High rates of cross-shard transactions can slow overall processing speed and reduce throughput, while uneven load distribution can overburden some shards and underutilize others, impacting overall efficiency. To address these issues, this paper proposes a sharding method named LCLB. This method employs the concept of label propagation to pre-shard accounts with frequent transactions into a designated shard, ensuring a lower ratio of cross-shard transactions. It also uses an elastic load balancing algorithm to dynamically adjust the distribution of accounts, ensuring a relatively balanced processing load across all shards. Our results indicate that LCLB significantly enhances blockchain sharding performance,not only enhancing transaction throughput but also significantly reducing transaction confirmation latency, while ensuring a balanced load across various shards.

关键词： Sharding Heuristic algorithms Throughput Load management Blockchains Partitioning algorithms Delays Resource management Robots Intelligent control

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