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A Federated Learning Framework with Blockchain-Based Auditable Participant Selection

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Computers, Materials & Continua 2024年第6期79卷 5125-5142页

作者： Huang Zeng Mingtian Zhang Tengfei Liu Anjia Yang College of cyber security Jinan UniversityGuangzhou510632China

Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model *** module enables a central server to select a subset of participants to performmodel training based on data and device *** doing so,selected participants are rewarded and actively perform model training,while participants that are detrimental to training efficiency and model accuracy are ***,in practice,participants may suspect that the central server may have miscalculated and thus not made the selection *** lack of trustworthiness problem,which can demotivate participants,has received little *** problem that has received little attention is the leakage of participants’private information during the selection *** will therefore propose a federated learning framework with auditable participant *** supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the *** the possibility of malicious campaigning and impersonation of participants,the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious ***,we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.

关键词： Federated learning internet of things participant selection blockchain auditability privacy

Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

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Computers, Materials & Continua 2024年第5期79卷 2105-2122页

作者： Mingze Li Diwen Zheng Shuhua Lu College of Information and cyber security People’s Public Security University of ChinaBeijing102600China

Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep ***,there have been stillmany challenges including crowd multi-scale variations and high network complexity,*** tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is ***,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual *** addition,a compound loss function is introduced for training themethod to improve global context information *** proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public *** accuracy is better than those of many advanced approaches,while the number of parameters is *** experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.

关键词： Crowd counting Res-connection multi-branch compound loss function

Heterophilic Graph Neural Network Based on Spatial and Frequency Domain Adaptive Embedding Mechanism

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1701-1731页

作者： Lanze Zhang Yijun Gu Jingjie Peng College of Information and cyber security People’s Public Security University of ChinaBeijing100038China

Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic *** GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggregation and ***,in heterophilic graphs,nodes from different categories often establish connections,while nodes of the same category are located further apart in the graph *** characteristic poses challenges to traditional GNNs,leading to issues of“distant node modeling deficiency”and“failure of the homophily assumption”.In response,this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks(SFA-HGNN),which integrates adaptive embedding mechanisms for both spatial and frequency domains to address the aforementioned ***,for the first problem,we propose the“Distant Spatial Embedding Module”,aiming to select and aggregate distant nodes through high-order randomwalk transition probabilities to enhance modeling *** the second issue,we design the“Proximal Frequency Domain Embedding Module”,constructing adaptive filters to separate high and low-frequency signals of nodes,and introduce frequency-domain guided attention mechanisms to fuse the relevant information,thereby reducing the noise introduced by the failure of the homophily *** deploy the SFA-HGNN on six publicly available heterophilic networks,achieving state-of-the-art results in four of ***,we elaborate on the hyperparameter selection mechanism and validate the performance of each module through experimentation,demonstrating a positive correlation between“node structural similarity”,“node attribute vector similarity”,and“node homophily”in heterophilic networks.

关键词： Heterophilic graph graph neural network graph representation learning failure of the homophily assumption

A Lightweight, Searchable, and Controllable EMR Sharing Scheme

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Computers, Materials & Continua 2024年第4期79卷 1521-1538页

作者： Xiaohui Yang Peiyin Zhao School of cyber security and Computer Hebei UniversityBaoding071000China

Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer fromprivacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharingscheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computationaloverhead of encryption and decryption reaches a lightweight constant level, and supports keyword search andpolicy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technologyis utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the datato achieve controllability of the data. Meanwhile, the data is re-encrypted by Intel Software Guard Extensions(SGX) technology to realize resistance to offline dictionary guessing attacks. In addition, blockchain technology isutilized to achieve credible accountability for abnormal behaviors in the sharing process. The experiments reflectthe obvious advantages of the scheme in terms of encryption and decryption computation overhead and storageoverhead, and theoretically prove the security and controllability in the sharing process, providing a feasible solutionfor the safe and efficient sharing of EMR.

关键词： Lightweight keyword search large attribute domain controllability blockchain

A Fair and Trusted Trading Scheme for Medical Data Based on Smart Contracts

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Computers, Materials & Continua 2024年第2期78卷 1843-1859页

作者： Xiaohui Yang Kun Zhang School of cyber security and Computer Hebei UniversityBaoding071000China

Data is regarded as a valuable asset,and sharing data is a prerequisite for fully exploiting the value of ***,the current medical data sharing scheme lacks a fair incentive mechanism,and the authenticity of data cannot be guaranteed,resulting in low enthusiasm of participants.A fair and trusted medical data trading scheme based on smart contracts is proposed,which aims to encourage participants to be honest and improve their enthusiasm for *** scheme uses zero-knowledge range proof for trusted verification,verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction,and realizes privacy *** the same time,the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction *** smart contract is used to complete the verification and game bargaining process,and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction ***,by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations,it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized *** experimental results show that the model improves the credibility and fairness of medical data transactions,maximizes social benefits,encourages more patients and medical institutions to participate in the circulation of medical data,and more fully taps the potential value of medical data.

关键词： Blockchain data transactions zero-knowledge proof game pricing

SAT-Based Automatic Searching for Differential and Linear Trails: Applying to CRAX

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Chinese Journal of Electronics 2024年第1期33卷 72-79页

作者： Yiyi HAN Caibing WANG Zhongfeng NIU Lei HU State Key Laboratory of Information security Institute of Information Engineering Chinese Academy of Sciences School of cyber security University of Chinese Academy of Sciences

Boolean satisfiability problem(SAT) is now widely applied in differential cryptanalysis and linear cryptanalysis for various cipher *** generated many excellent results for some ciphers,for example,*** this research,we study the differential and linear propagations through the operations of addition,rotation and XOR(ARX),and construct the SAT *** apply the models to CRAX to search differential trails and linear trails *** this sense,our contnbution can be broadly divided into two *** give the bounds for differential and linear cryptanalysis of Alzette both up to 12 steps,by which we present a 3-round differential attack and a 3-round linear attack for *** construct a 4-round key-recovery attack for CRAX with time complexity289times of 4-round encryption and data complexity 225.

关键词： Ciphers Encryption Complexity theory Time complexity

Generic attacks on small-state stream cipher constructions in the multi-user setting

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cybersecurity 2024年第3期7卷 69-79页

作者： Jianfu Huang Ye Luo Qinggan Fu Yincen Chen Chao Wang Ling Song College of cyber security Jinan UniversityGuangzhou 510632China National Joint Engineering Research center of Network security Detection and Protection Technology Jinan UniversityGuangzhou 510632China

Small-state stream ciphers(SSCs),which violate the principle that the state size should exceed the key size by a factor of two,still demonstrate robust security properties while maintaining a lightweight *** ciphers can be clas-sifed into several constructions and their basic security requirement is to resist generic attacks,ie.,the time-mem-ory-data tradeoff(TMDTO)*** this paper,we investigate the security of small-state constructions in the multi-user *** on it,the TMDTO distinguishing attack and the TMDTO key recovery attack are developed for such a *** is shown that SSCs which continuously use the key can not resist the TMDTO distinguishing ***,SSCs based on the continuous-IV-key-use construction cannot withstand the TMDTO key recovery attack when the key length is shorter than the IV length,no matter whether the keystream length is limited or ***,We apply these two generic attacks to TinyJAMBU and DRACO in the multi-user *** TMDTO distinguish-ing attack on TinyJAMBU with a 128-bit key can be mounted with time,memory,and data complexities of 264,248,and 232,*** attack is comparable with a recent work on ToSC 2022,where partial key bits of TinyJAMBU are recovered with more than 250 users(or keys).As DRACO's IV length is smaller than its key length,itis vulnerable to the TMDTO key recovery *** resulting attack has a time and memory complexity of both 2112,which means DRACO does not provide 128-bit security in the multi-user setting.

关键词： Small-state stream ciphers TMDTO attacks Multi-user setting

Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

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Computers, Materials & Continua 2024年第5期79卷 3023-3045页

作者： Zhenxiang He Zhenyu Zhao Ke Chen Yanlin Liu School of cyber security Gansu University of Political Science and LawLanzhou730070China

The fast-paced development of blockchain technology is ***,the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain *** smart contract vulnerability detection primarily relies on static analysis tools,which are less efficient and *** deep learning methods have improved detection efficiency,they are unable to fully utilize the static relationships within ***,we have adopted the advantages of the above two methods,combining feature extraction mode of tools with deep learning ***,we have constructed corresponding feature extraction mode for different vulnerabilities,which are used to extract feature graphs from the source code of smart ***,the node features in feature graphs are fed into a graph convolutional neural network for training,and the edge features are processed using a method that combines attentionmechanismwith gated ***,the revised node features and edge features are concatenated through amulti-head *** result of the splicing is a global representation of the entire feature *** method was tested on three types of data:Timestamp vulnerabilities,reentrancy vulnerabilities,and access control vulnerabilities,where the F1 score of our method reaches 84.63%,92.55%,and 61.36%.The results indicate that our method surpasses most others in detecting smart contract vulnerabilities.

关键词： Blockchain smart contracts deep learning graph neural networks

A New Solution to Intrusion Detection Systems Based on Improved Federated-Learning Chain

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Computers, Materials & Continua 2024年第6期79卷 4491-4512页

作者： Chunhui Li Hua Jiang School of Computer and Electronic Information Guangxi UniversityNanning530000China cyber security and Information center Guangxi UniversityNanning530000China

In the context of enterprise systems,intrusion detection(ID)emerges as a critical element driving the digital transformation of *** systems spanning various sectors of enterprises geographically dispersed,the necessity for seamless information exchange has surged *** existing cross-domain solutions are challenged by such issues as insufficient security,high communication overhead,and a lack of effective update mechanisms,rendering them less feasible for prolonged application on resource-limited *** study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side *** this framework,individual nodes solely engage in training local data and subsequently amalgamate the final model employing a federated learning algorithm to uphold enterprise systems with efficiency and *** curtail the resource utilization of blockchains and deter malicious nodes,a node administration module predicated on the workload paradigm is introduced,enabling the release of surplus resources in response to variations in a node’s contribution *** encountering an intrusion,the system triggers an alert and logs the characteristics of the breach,facilitating a comprehensive global update across all nodes for collective *** results across multiple scenarios have verified the security and effectiveness of the proposed solution,with no loss of its recognition accuracy.

关键词： Cross-domain collaboration blockchain federated learning contribution value node management release slack resources