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Provably Secure Authenticated key-Management Mechanism for e-Healthcare Environment

IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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Spatial-Spectral Transformer for Local and Global Hyperspectral Image Classification

Spatial-Spectral Transformer for Local and Global Hyperspect...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： MengXin Cao Guixin Zhao 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 Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Deep learning techniques have become increasingly prevalent in hyperspectral image (HSI) classification. However, convolutional neural network (CNN)-based approaches often struggle with inefficient utilization of spectral sequence data and inadequate processing of fine-grained spectral details. In contrast, transformer models are inherently designed to capture sequence dependencies across spectral bands. Yet, most existing approaches tend to focus on either local or global feature extraction separately, which limits their ability to fully exploit the spectral information. To overcome these limitations, we propose the Local and Global Fusion Spectral Transformer (LGFST), an innovative architecture that combines Swin Transformer blocks, standard Transformer blocks, and a novel Local-Global Fusion (LGF) module. This architecture is designed to simultaneously capture both local and global features, thereby improving the extraction of detailed spectral information. The Swin Transformer block, integrated with patch merging and hierarchical attention mechanisms, expands the receptive field and enhances local feature representation through both intra- and cross-window interactions. Meanwhile, the Transformer block performs self-attention across all tokens to extract global features. The fusion of local and global features from these two blocks effectively improves the texture and discriminative power of spectral data, leading to enhanced classification performance, especially in scenarios with dense class distributions. Finally, the LGF module facilitates interaction between local and global feature sets, ensuring a seamless integration of information from both blocks. Extensive experiments on three benchmark HSI datasets demonstrate that LGFST outperforms conventional transformer models and sets new benchmarks in performance compared to state-of-the-art methods.

关键词： Merging Benchmark testing Transformers Feature extraction Distortion Sensors Data mining Standards Hyperspectral imaging Image classification

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An Early Detection Model of Prostate Cancer Based on MRI 9

An Early Detection Model of Prostate Cancer Based on MRI

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

作者： Liu, Yuankun Xing, Lumin Wang, Di Nie, Xin Zhang, Xueyan Wu, Di Liu, Wenjian The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences 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 Computer Networks Jinan China Chongqing Jianzhu College Chongqing China Shandong Youth University of Political Science School of Information Work Office Jinan China University of Southern Queensland School of Mathematics Physics and Computing QLD Australia

ISBN: (纸本)9798350391367

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively. © 2024 IEEE.

关键词： Urology

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An Encryption Algorithm Cycle Identification Method Based on Bit Execution

An Encryption Algorithm Cycle Identification Method Based on...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yunge Sun Gaolei Du Junpeng Qu Yong Fu 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 Quan Cheng Laboratory 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

Ransomware uses symmetric encryption algorithms to lock user files to extort money. This paper proposes a cryptographic algorithm loop identification method based on bit-precise execution, aiming to solve the problem of identifying unknown loops. The structure of the loop body of encryption algorithms is fixed and usually includes nonlinear operations and linear operations. This article converts the loop body in the unknown software into a set of Boolean formulas, and converts the malware into a register data flow through the symbolic executor, thereby converting it into a set of Boolean formulas for execution. Execute known encryption algorithms with bit accuracy and establish a Boolean formula input-output relationship library for cryptographic components. The two sets of Boolean formulas are passed through a designed input matrix to achieve the Boolean formula mapping and input variable mapping of the confirmation algorithm, which can effectively judge the encryption algorithms in some malware.

关键词： Deformation Input variables Software algorithms Merging Data science Libraries Encryption Registers Ransomware Matrix converters

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A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Cao, Xiaoya Chen, Zhenya Liu, Weisheng Liu, Yuanlong Wang, Wenting Wang, Xin Yang, Ming Qilu University of Technology Shandong Academy of Sciences 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 Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350312270

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy. © 2023 IEEE.

关键词： System stability

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

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IEEE Transactions on Computers 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. © 1968-2012 IEEE.

关键词： Scalability

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Dynamic detection technology of encryption algorithm based on Pin

Dynamic detection technology of encryption algorithm based o...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Junpeng Qu Gaolei Du Yunge Sun Yong Fu 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 Quan Cheng Laboratory 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

Aiming at the problem that malicious software uses encryption algorithm to hide its own malicious code and avoid detection, a dynamic detection technology based on Pin encryption algorithm is proposed. Use Pin to get the instruction flow of executable file running, then identify the function call in the instruction flow to get the function call framework, find the loop body in the calling function, and then carry out the encryption algorithm instruction detection and encryption algorithm constant detection on the loop body. If the detection passes, it can be considered that a component of the encryption algorithm is found. Then the complete encryption algorithm is found according to the generated function call framework. Experiments show that this method can effectively identify the encryption algorithms hidden in executable files.

关键词： Heuristic algorithms Software algorithms Cyberspace Data science Feature extraction Malware Encryption Pins

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FedSimRep: A Federated Learning Method Based on Data Representation Selection 25

FedSimRep: A Federated Learning Method Based on Data Represe...

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Proceedings of the 2025 3rd International Conference on Communication networks and Machine Learning

作者： Falu Zhai Lu Wu Chunxiao Wang Tongyang Hao Shucang Zang 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 Shandong China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (纸本)9798400713231

As an emerging distributed technology, Federated learning enables model training while protecting privacy without sharing local data. However, the heterogeneity of geographic locations and devices leads to response delays from stragglers. Client grouping in federated learning can tolerate stragglers from a global perspective, but differences in data distribution within groups may result in local performance bias. To address this issue, we propose a federated learning method called FedSimRep, which selects representative clients based on data distribution similarity. FedSimRep performs group training by collecting readiness signals from topological communications and maintains synchronization tables and graphs to track training progress. Additionally, it selects representative clients by leveraging the relationship between local optimization similarity and data distribution similarity to optimize the global model. Experiments and theoretical analyses on public datasets such as MNIST, CIFAR-10, and UEU-Fed demonstrate that FedSimRep achieves significant advantages in terms of accuracy and efficiency.

关键词： Client Grouping

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An automated construction method for symmetric encryption round function models based on dynamic analysis

An automated construction method for symmetric encryption ro...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Gaolei Du Yunge Sun Junpeng Qu Yong Fu 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 Quan Cheng Laboratory 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

With the deepening of informatization, the incidence of cybercrimes utilizing malicious software for attacks is on the rise. This paper addresses the detection issue of symmetric encryption in malicious software from the perspective of cryptography, proposing an automated method for constructing a symmetric encryption round function model based on dynamic analysis. This method tracks data flow at the granularity of registers to characterize Directed Acyclic Graph (DAG) construction of round function models. Initially, dynamic execution traces of binary files are captured using Intel Pin, identifying loops within them. Data flow tracking at the register level within loops is performed, creating DAG, and analyzing whether the loops exhibit characteristics of symmetric cipher round functions, thus constructing the round function model. This approach enables the detection of symmetric encryption within binary files and the construction of round function models, offering a novel solution for fine-grained symmetric cipher recognition and laying a foundation for detecting malicious software containing symmetric encryption.

关键词： Analytical models Ciphers Tracking loops XML Data models Malware Encryption Registers Trajectory Pins

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Outsourced Secure Cross-Modal Retrieval based on Secret Sharing for Lightweight Clients

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IEEE Internet of Things Journal 2025年

作者： Niu, Ziyu Wang, Hao Li, Zhi Su, Ye Xu, Lijuan Zhang, Yudi Susilo, Willy Shandong Normal University School of Information Science and Engineering Jinan China University of Wollongong School of Computing and Information Technology Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education National Supercomputer Center in Jinan 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

Cross-modal retrieval is a technique that uses one modality to query another modality in multimedia data (e.g., retrieving images based on text, or retrieving text based on images). It can break down the barriers between different modalities and achieve seamless information connection. Secure cross-modal retrieval focuses on privacy issues in cross-modal retrieval, including private data of data owners and private query requests of users. Current work on secure cross-modal retrieval protects private information through homomorphic encryption, which makes the efficiency of the retrieval phase not ideal. Therefore, the conflict between retrieval efficiency and security has become an important issue that needs to be resolved in secure cross-modal retrieval. We propose a scheme to achieve secure cross-modal retrieval in the form of secret sharing in the IoT environment. In the scheme, the data owner (DO) can secretly divide all the original data into two parts and upload them to two non-collusive cloud servers respectively. The servers store the data and provide cross-modal retrieval for users. The security of the scheme is proved under semi-honest model, and the experiments show that our scheme is more efficient than previous work in the search phase. When the query dimension is 512 and the number of latent factors is 500, the search time is reduced by more than half compared with previous work. © 2014 IEEE.

关键词： Sensitive data

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