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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

Verification of quantum networks using the GHZ paradox

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Communications in Theoretical Physics 2024年第10期76卷 57-63页

作者： Huan Ye Xue Yang Ming-Xing Luo School of Information science and Technology Southwest Jiaotong University School of computer science and cyber security Chengdu University of Technology

The Greenberger–Horne–Zeilinger(GHZ) paradox shows that it is possible to create a multipartite state involving three or more particles in which the measurement outcomes of the particles are correlated in a way that cannot be explained by classical physics. We extend it to witness quantum networks. We first extend the GHZ paradox to simultaneously verify the GHZ state and Einstein–Podolsky–Rosen states on triangle networks. We then extend the GHZ paradox to witness the entanglement of chain networks consisting of multiple GHZ states. All the present results are robust against the noise.

关键词： GHZ paradox quantum entanglement quantum network

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

Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

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science China(Information sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG Institute of Information Engineering Chinese Academy of Sciences School of cyber security University of Chinese Academy of Sciences School of computer science and Technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift malicious traffic identification variational autoencoder intrusion detection cyberspace security

Enhancing Image Description Generation through Deep Reinforcement Learning:Fusing Multiple Visual Features and Reward Mechanisms

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computers, Materials & Continua 2024年第2期78卷 2469-2489页

作者： Yan Li Qiyuan Wang Kaidi Jia School of cyber security Gansu University of Political Science and LawLanzhou730070China

Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually *** study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of *** method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more *** approach comprises three key ***,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature ***,the Transformer model is engaged to derive contextual semantic features from textual ***,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward *** with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is ***-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image *** research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.

关键词： Image description deep reinforcement learning attention mechanism

High-Secured Image LSB Steganography Using AVL-Tree with Random RGB Channel Substitution

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computers, Materials and Continua 2024年第1期81卷 183-211页

作者： Njoum M. Sulaiman R. Shukur Z. Qamar F. Center of cyber security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia computer science and cyber security Faculty of Engineering and Information Technology BirZeit University Ramallah P.O. Box 14 BirZeit Palestine

Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden data. This property makes it difficult for steganalysts’ powerful data extraction tools to detect the hidden data and ensures high-quality stego image generation. However, using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical equations. In addition, these numbers may cluster in certain ranges. The hidden data in these clustered pixels will reduce the image quality, which steganalysis tools can detect. Therefore, this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego image. This paper employs the Adelson-Velsky and Landis (AVL) tree data structure algorithm to implement the randomization pixel selection technique for data concealment. The AVL tree algorithm provides several advantages for image steganography. Firstly, it ensures balanced tree structures, which leads to efficient data retrieval and insertion operations. Secondly, the self-balancing nature of AVL trees minimizes clustering by maintaining an even distribution of pixels, thereby preserving the stego image quality. The data structure employs the pixel indicator technique for Red, Green, and Blue (RGB) channel extraction. The green channel serves as the foundation for building a balanced binary tree. First, the sender identifies the colored cover image and secret data. The sender will use the two least significant bits (2-LSB) of RGB channels to conceal the data’s size and associated information. The next step is to create a balanced binary tree based on the green channel. Utilizing the channel pixel indicator on the LSB of the green channel, we can conceal bits in the 2-LSB of the red or blue channel. The first four levels of the data structure tree will mask the data size, while subsequent levels will conceal the remaining digits of s

关键词： AVL tree capacity Image steganography imperceptibility peak signal-to-noise ratio (PSNR) pixel random selection (PRS)

Provably secure attribute-based authenticated encryption with keyword search from ideal lattices

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science China(Information sciences) 2024年第1期67卷 308-309页

作者： Lisha YAO Lin HOU Jian WENG Feixiang ZHAO College of cyber security Jinan University

A public-key authenticated encryption with keyword search(PAEKS)[1, 2] system overcomes the problem of an inside adversary forging searchable ciphertexts and guessing the keyword information from the given token. This system allows the sender to encrypt and authenticate a ***, traditional PAEKS schemes require the sender to specify the intended receiver in advance, losing much flexibility.

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CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

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science China(Information sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA School of cyber Engineering Xidian University School of computer science and Technology Xidian University Department of computer and Information science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and *** in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

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