Table of Content

30 July 2024, Volume 42 Issue 4

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Blockchain

A Blockchain Scheme for Vehicular Internet of Things

SHI Zhigang, HUANG Jianhua, LI Tianqi

2024, 42(4):  549-568.  doi:10.3969/j.issn.0255-8297.2024.04.001

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At present, the industry seeks to use blockchain to enhance the reliability, trust and security of vehicular internet of things (VIoT) applications. However, the performance limitations pose challenges for blockchain in adapting to the mobility and real-time requirements of VIoT. To solve the above problems, this paper proposes a tree-blockchain consensus (TBC) mechanism based on virtual sharding and directed acyclic graph (DAG), which uses fixed trusted nodes to reach consensus to reduce dependence on mobile vehicle nodes. A tree-blockchain is built based on DAG to achieve parallel validation and uplink of blocks. For the tree-blockchain, a virtual sharding validation mechanism based on matching code is proposed. The validating nodes and transactions are logically grouped and paired through VRF functions and simple hash operations, ensuring the randomness of transaction sharding, reducing the computational overhead of the validation process, and improving consensus efficiency. Security analysis demonstrates that TBC can effectively deal with the common malicious information attacks, Sybil attacks and man-in-the-middle attacks in the network. Simulation results further show that TBC outperforms traditional consensus algorithms, meeting the specific application requirements of VIoT.

Trusted Blockchain Automation Protocol Based on Domain Programming Model

LIU Shaojie, ZHAO Hongbo, LIU Han

2024, 42(4):  569-584.  doi:10.3969/j.issn.0255-8297.2024.04.002

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Blockchain automation technology effectively addresses the dilemma of blockchain’s incapability to automatically execute smart contract programs, and expands the application scenarios of blockchain. However, existing automation solutions often suffer from complex task definitions, lack of provability in task execution, and the inability to support off-chain data, leading to high entry barriers and low trustworthiness. In response, this paper proposes a trustworthy blockchain automation protocol called Specy Network. This protocol first combines domain-specific programming models with a trusted execution environment to design a domain-specific language tailored for blockchain automation scenarios. It achieves provability in condition checks, thereby simplifying task definitions while enhancing the reliability of task verification. Secondly, it optimizes role interactions and task lifecycles in blockchain automation business, improving the stability of rotocol implementation. Finally, the proposed protocol is implemented, and its feasibility is validated through specific use cases.

A Domain Adaptive Security Analysis Framework for Smart Contracts

WANG Na, ZHU Huijuan, SONG Xiangmei, FENG Xia

2024, 42(4):  585-597.  doi:10.3969/j.issn.0255-8297.2024.04.003

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The available smart contract vulnerability detection schemes mostly rely on expert-defined rules, which lack flexibility and struggle with new unknown vulnerabilities. To address this challenge, we present a novel framework called domain adaptive security analysis framework (DASAF). Firstly, we obtain the execution logic of smart contract opcodes and convert them into meaningful sequential features. Secondly, to overcome the inherent data bias in deep learning models, which leads to model aging and difficulty in achieving strong generalization performance due to insufficient labeled samples in new unknown vulnerabilities, the DASAF framework introduces adversarial generative network structure and domain adaptation techniques. Finally, we evaluate the effectiveness of the DASAF framework in the field of smart contract vulnerability analysis and detection using a public benchmark dataset, and compare it with similar schemes. The experimental results demonstrate the superiority of the DASAF framework over comparable approaches.

Auditable and Traceable Blockchain Privacy Protection Model under Zero-Knowledge Proof

WU Meng, QI Yong

2024, 42(4):  598-612.  doi:10.3969/j.issn.0255-8297.2024.04.004

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In order to address the issues of sensitive data exposure due to shared ledgers among nodes in a blockchain network, alongside the inability to audit and trace encrypted privacy data, a blockchain privacy protection model based on directed graphs and zeroknowledge proofs has been proposed. This model extends the open-source Hyperledger Fabric framework and effectively inherits the features of Fabric. By encrypting on-chain transaction information and utilizing Pedersen commitments and Schnorr-type zero-knowledge proofs, it generates proofs of balance, traceability, asset ownership, and consistency to provide fast and verifiable privacy data audits. The model utilizes a directed graph structure to construct a transaction graph, thus achieving traceability of transaction information on the blockchain. Moreover, it generates proofs to validate the correctness of forward tracing transactions. Experimental results demonstrate that the proposed model achieves complete audit and traceability on Fabric at a cost of less than 10% throughput, outperforming existing related models.

Medical Data Classification Encryption Sharing Scheme Based on Blockchain

XIA Xiaoliang, QIN Zhi, WAN Wunan, ZHANG Shibin, ZHANG Jinquan

2024, 42(4):  613-628.  doi:10.3969/j.issn.0255-8297.2024.04.005

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When sharing medical data, the volume of shared data often exceeds the necessary amount, leading to significant computational overhead when encrypting a large number of metadata. This paper proposes a medical data classification encryption and sharing scheme based on blockchain, which integrates attribute-based encryption and blockchain to facilitate access control and data sharing of medical information. First, the entire medical dataset is classified into medical metadata according to the basic information, medical departments and disease types, enabling fine-grained access control. Then, a data access strategy classification algorithm is proposed, which divides the data access strategy into attribute encryption strategy and blockchain access strategy. Multiple attribute encryption strategies are combined to reduce the computational cost of constructing the access structure tree. Smart contract controls the access of the data on the chain according to the blockchain access policy, and the authority is revoked by modifying the blockchain policy. Finally, the security analysis and simulation experiments validate the feasibility and efficiency of the proposed scheme.

Value-Driven Ethereum Transaction Tracing Rank Method

LEI Ming, LIN Yijing, GAO Zhipeng

2024, 42(4):  629-641.  doi:10.3969/j.issn.0255-8297.2024.04.006

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Blockchain offers users anonymity and facilitates the decentralized transfer of value. However, malicious attackers might employ phishing or other fraudulent methods to steal assets and withdraw them from cryptocurrency exchanges by designing complex transaction interactions. In this paper, we address this challenge by presenting a valuedriven transaction tracking and ranking method tailored for Ethereum. In this approach, we collect a transaction dataset of up to 27 GB from 12 Ethereum attack cases with fraud amounts exceeding one million US dollars, and construct an address graph to describe the relationship between addresses. We then invoke token liquidity pool data from the onchain data to represent the historical price of assets and determine the weight coefficients for transactions in the graph. Finally, we introduce a dynamic residual scaling mechanism based on value proportion to optimize the address graph structure by optimal value flow paths. Experimental results show that the proposed method achieves a recall rate of 89.24%, which represents a notable improvement of 7%, 20%, and 37% over transaction tracing rank (TTR), APPR, and Haircut algorithms, respectively, confirming the effectiveness of the proposed method.

Dynamic Role Identification and Evolutionary Analysis of Blockchain Game Ecosystems: A Case Study of Axie Infinity

LIU Kai, WANG Jiaxin, MAO Qian'ang, CHEN Yufei, YAN Jiaqi

2024, 42(4):  642-658.  doi:10.3969/j.issn.0255-8297.2024.04.007

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Given the intricate nature of blockchain game ecosystems, this study proposes a new role identification method based on a time-series directed weighted network. Specifically, this method first designs a new node voting algorithm called ChainVoteRank to identify key basic roles, and then uncovers potential hidden roles by combining hierarchical clustering algorithm with multi-feature fusion. Focusing on Axie Infinity, a “play-to-earn” (P2E) mode blockchain game, the research findings indicate that the existence of six fundamental roles within the P2E mode blockchain game ecosystem: laborers, regular players, managers, breeders, traders, and institutional organizations. Compared with traditional methods, the proposed method can better identify the major user roles in the blockchain game ecosystem. Additionally, the study reveals the evolutionary process of roles within the P2E mode blockchain game ecosystem and the roles played by each role in different phases. Furthermore, a discussion of the escalating wealth disparity in P2E ecosystem is provided.

Communication Engineering

Encrypted Traffic Classification Based on Attention Temporal Convolutional Network

JIN Yanliang, CHEN Yantao, GAO Yuan, ZHOU Jiahao

2024, 42(4):  659-672.  doi:10.3969/j.issn.0255-8297.2024.04.008

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Aiming at the problem that most current encrypted traffic classification methods ignore the timing characteristics in the traffic and the model efficiency, we propose an efficient classification method based on attention temporal convolutional network (ATCN). This method first embeds content information and timing information into the model to enhance the representation of encrypted traffic. Then it utilizes temporal convolutional network to capture effective features in parallel to increase training speed. Finally, we introduce attention mechanism to establish dynamic feature aggregation to optimize model parameters. Experimental results show the superior performance of our proposed method over the baseline in two classification tasks, achieving accuracy of 99.4% and 99.8%, respectively, while reducing the number of model parameters to a maximum of 15% of the baseline. Finally, a fine-tuning method based on transfer learning is introduced to the ATCN, which provides a novel approach for zero-day traffic processing in traffic classification.

Research on Vehicle Monitoring Using Pavement Surface Based on Grating Array Vibration Sensing

SONG Ke, LIU Jiaxin, HU Wenyu, ZHOU Ai

2024, 42(4):  673-683.  doi:10.3969/j.issn.0255-8297.2024.04.009

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This paper addresses the limitations of full-time domain monitoring methods for pavement by introducing a road vehicle monitoring approach based on grating array vibration sensing. Grating array vibration sensing utilizes grating array technology, where a grating array sensor optical cable is laid beneath the road surface. Through grating array demodulation equipment, vehicle vibration information is extracted. Theoretical analysis and experimental research on vehicle vibration data reveal that, with effective filtering, the experimental setup can accurately capture vehicle trajectories and ascertain vehicle speed and location. Furthermore, the paper proposes an image-based vehicle tracking and recognition algorithm to mitigate the impact of harsh environmental conditions, enabling comprehensive tracking and monitoring of the entire monitoring section in real-time. This methodology paves the way for advancements in intelligent highway systems.

Signal and Information Processing

Exploration Analysis of Fire Drives in Different Chinese Ecosystems Based on Google Earth Engine

MA Dan, TANG Zhiwei, MA Xiaoyu, SHAO Erhui, HUANG Dacang

2024, 42(4):  684-694.  doi:10.3969/j.issn.0255-8297.2024.04.010

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Focused on studying wildfire and driving forces across various ecosystems on a large scale, this paper presents a novel method utilizing the google earth engine (GEE) platform. Firstly, the fire information for resource management system database, Sentinel-2 images and driving factor information in four different Chinese ecosystems were accessed on online via GEE platform. Then, the differential normalized burn ratio was extracted from Sentinel-2 images to sieve fire spots. Three machine learning algorithms, namely random forest, support vector machine and augmented regression tree, were used to classify fire locations. Furthermore, we determined the optimal-performing classification algorithms for each ecosystem and assessed variable importance. The results showed that random forest performed best with accuracy exceeding 92% among the three machine methods and the fire drivers varied significantly among four different ecosystems. In Changzhi City of Shanxi Province, and the Great Xing′an Mountains of Inner Mongolia, population distribution and maximum temperature were identified as the most influential drivers, respectively. While for Liangshan Yi Autonomous Prefecture in Sichuan Province and Ganzhou City in Jiangxi Province, the palmer drought index and soil moisture emerged as the primary drivers. This study demonstrates the efficacy of the proposed GEE-based method in studying wildfire and driving forces across different ecosystems in large scale regions.

Fusion of Point-Cloud and Image for Road Segmentation Using CNN and Transformer

HUA Yitan, HUANG Yingping, GUO Wenhao

2024, 42(4):  695-708.  doi:10.3969/j.issn.0255-8297.2024.04.011

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To address the problem of low accuracy and inaccurate road edge segmentation caused by the susceptibility of road detection models to light and shadows, we propose a road segmentation algorithm based on a hybrid of Transformer and convolutional neural network models, utilizing RGB images and 3D LIDAR point clouds as inputs to enhance the precise perception of driving roads for autonomous vehicles. Experimental results on the KITTI road dataset demonstrate the superior segmentation accuracy of the proposed method compared with existing road detection models.

Computer Science and Applications

Intelligent Synthetic Voice Speaker Verification Method Based on Group-Res2Block

LI Fei, SU Zhaopin, WANG Niansong, YANG Bo, ZHANG Guofu

2024, 42(4):  709-722.  doi:10.3969/j.issn.0255-8297.2024.04.012

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The existing speaker verification task is primarily based on natural speech conditions, rendering it unsuitable for intelligent speech synthesis. In response, this paper proposes an intelligent synthetic voice speaker verification method based on Group-Res2Block. Firstly, the Group-Res2Block structure is designed, integrating the current group with adjacent front and rear groups to foster a stronger contextual connection of the speaker’s local characteristics. Secondly, a multi-scale channel attention feature fusion mechanism with parallel structure is designed. This mechanism employs various-sized convolution kernels to select features of the same level in the channel dimension, thereby extracting more expressive speaker features and avoiding information redundancy. Finally, a multi-scale attention feature fusion mechanism of serial structure is designed, and a layer structure is constructed to integrate the deep and shallow features as a whole and give different weights to obtain the optimal feature expression. To verify the effectiveness of the proposed feature extraction network, this paper constructs two kinds of intelligent synthetic speech datasets in Chinese and English. Through ablation and comparative experiments, it is shown that the proposed method outperforms others on evaluation metrics such as accuracy (ACC), equal error rate (EER) and minimum detection cost function (minDCF) for the task. Furthermore, the test results of the generalization performance of the model verify its applicability to unknown intelligent speech algorithms.