CCF-B
【影响因子】9.551
【主题类别】
--
【Abstract】Vehicle recognition constitutes a foundational technology within intelligent transport systems (ITS), enabling real-time recognition, classification, and tracking of vehicles. With the characteristics of low construction cost, flexible deployment and strong environment adaptability, unmanned aerial vehicle (UAV) is increasingly leveraged for vehicle target recognition, acts as the air part of future intelligent transport systems (ITS) for traffic management, accident handling and vehicle order management, and provides a more efficient, safe and sustainable transport mobility solutions in future ITS. Promoted by the massive number of intelligent vehicles and growing demands of connected vehicles in ITS, continuous and high-fidelity spatio-temporal monitoring of vehicle movement is expected in future ITS, raising the pursuit of higher vehicle recognition performance. As a typical distributed training framework, federated learning (FL) is a desired paradigm to improve sensing performance with the communication of sensing parameters for UAV-enabled ITS. Due to the heterogeneity of sensing data in the cooperative UAV-enabled ITS, the non-independent identically distribution (Non-IID) issue is inevitable. The existing data augmentation works aimed at Non-IID issue in FL utilize single-agent reinforcement learning (SARL), where the local model parameters are input into a central network, resulting in the model privacy leakage problem. To deal with the above issue, a multi-agent reinforcement learning (MARL) algorithm is applied to optimize the training accuracy and data augmentation efficiency for UAV in ITS. Moreover, a decentralized blockchain-based FL (BFL) framework is proposed to avoid the single-point failure in UAV-enabled ITS. The experiments are conducted on the generated vehicle dataset (VRID) and the simulation results indicate that our proposed algorithm exhibits a superior performance than the benchmark algorithms, especially in terms of higher vehicle target recognition accuracy and lower communication overhead, which provides a significant technology support for vehicle identification and tracking in future ITS.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Training; Autonomous aerial vehicles; Sensors; Data models; Target tracking; Privacy; Reinforcement learning; Monitoring; Data privacy; Accuracy; Intelligent transport systems; federated learning; multi-agent reinforcement learning; unmanned aerial vehicle; vehicle recognition and tracking
【发表时间】2025
【收录时间】2025-09-17
【文献类型】
CCF-B
【影响因子】9.551
【主题类别】
--
【Abstract】The Internet of Vehicles (IoV), the latest generation of Vehicular Ad-hoc Networks (VANET) enables real-time, intelligent communication between vehicles and nearby transport infrastructures like roadside units, cloud servers, etc. It supports secure Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and other communication forms, which are vital in intelligent transportation systems. However, the dependence on wireless channels introduces risks, such as Man-In-The-Middle (MITM) attacks, replay attacks, and impersonation attacks, that lead to potential data leakage. Furthermore, many existing schemes rely on a centralized Trusted Authority (TA) for mutual authentication, which creates scalability limitations and increases latency. To address these challenges, this paper proposes a privacy-preserving mutual authentication and key agreement protocol for IoV using blockchain and a multi-TA network, which enables decentralized V2V and V2I authentication. Additionally, a lightweight Few-Shot Learning (FSL)-based module is integrated at the Zone Manager (ZM) level to classify real-time traffic conditions based on limited labeled samples. This enhances traffic control without compromising security. The distributed ledger managed by multiple TAs ensures synchronized access to authenticated credentials, facilitating secure communication across different zones. The security and performance analyses demonstrate the proposed scheme's security and efficiency over existing methods.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Authentication; Real-time systems; Protocols; Security; Blockchains; Computational modeling; Vehicle dynamics; Scalability; Accuracy; Registers; Cryptography; privacy-preservation; smart contract; multi-layer perceptron; prototype generator
【发表时间】2025
【收录时间】2025-09-17
【文献类型】
CCF-B
【影响因子】8.233
【主题类别】
--
【Abstract】The rapid growth of Ethereum has spurred widespread adoption of smart contracts, enabling substantial financial transactions. Once deployed on the blockchain, smart contracts are immutable, rendering them unmodifiable even if vulnerabilities are present. In recent years, numerous attacks exploiting these vulnerabilities have caused significant financial losses. Although prior research has improved vulnerability detection in source code or bytecode before deployment, identifying attacks that exploit vulnerabilities during the execution phase after deployment remains a significant challenge. These challenges arise from the limited adaptability of predefined detection rules and an overreliance on opcode sequence names, which often neglects a comprehensive analysis of opcode sequence properties. In this study, we propose an advanced multidimensional feature fusion technique designed to detect attacks during the execution phase of smart contracts. By leveraging deep learning, our approach enhances detection accuracy through a comprehensive analysis of attack behaviors across four dimensions: operation objects, action behaviors, functional categories, and gas consumption. Extensive experiments demonstrate that our method achieves a detection accuracy of 97.21% and a weighted F1-score of 97.21%, confirming its effectiveness in identifying attacks.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Smart contracts; Opcode sequences; Feature fusion; Vulnerabilities; Attack detection
【发表时间】2026
【收录时间】2025-09-17
【文献类型】
【Author】 Meng, Guoming Zhang, Leyou
【影响因子】3.721
【主题类别】
--
【Abstract】With the increasing emphasis on data circulation and value realization, privacy-preserving computation has become a critical enabler for cross-organizational data collaboration. This survey focuses on Private Set Intersection (PSI) techniques within the framework of Secure Multi-Party Computation (SMPC), systematically reviewing the theoretical foundations and technological evolution of PSI as a fundamental privacy-preserving protocol. We first construct a technical stack of PSI protocols, elucidating the cryptographic principles that enable efficient set operations while preserving data confidentiality. Furthermore, we explore the synergistic integration of PSI with blockchain and federated learning, highlighting innovative paradigms for addressing privacy challenges in decentralized environments. Notably, in response to emerging threats posed by quantum computing, this work analyzes the design of post-quantum PSI protocols based on pseudorandom quantum states. Through empirical studies in representative application scenarios-such as collaborative medical analytics, financial risk modeling, and government data sharing-this survey not only demonstrates the practical value of PSI but also underscores its pivotal role in building a trustworthy data collaboration ecosystem. As computational paradigms continue to evolve, PSI is poised to achieve breakthroughs in the multi-objective optimization of privacy, efficiency, and security, thereby offering robust privacy-preserving solutions across various industries.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Secure multi-party computation; Private set intersection; Cryptography; Data sharing; Privacy preservation
【发表时间】2026
【收录时间】2025-09-17
【文献类型】
【影响因子】
【主题类别】
--
【Abstract】Due to the immutable nature of smart contracts, online contract diagnosis is the only viable approach for revealing vulnerabilities in deployed contracts. Existing online approaches face significant challenges in terms of efficiency, adaptability, and reliance on vulnerability labels. This paper proposes ConWatcher+, a new adaptive and label-efficient online contract diagnosis framework from the diffusion perspective, which is capable to detect yet unknown attacks under evolving tactics without reliance on vulnerability labels. ConWatcher+ simulates the Advanced Persistent Threat (APT) tactics commonly used in yet unknown attacks by continuously applying minor perturbations to legitimate interaction behaviors. It then reversely learns the denoising process, guided by potential logic vulnerabilities (i.e., functionality dependencies), to adaptively identify stealthy anomalies and detect yet unknown attacks without needing vulnerability labels. ConWatcher+ proceeds in five steps. First, real-time data extraction. We design a cost-effective contract runtime information collector, incorporating on-demand data retrieval and event-driven data update mechanisms to reduce communication overhead in online contract diagnosis. Second, interaction behavior modeling. Via bytecode-level, account-level, revenue-level modeling, and side-channel level behavior modeling, we propose behavior-aware multivariate time series model to accurately represent long-term contract interactions with multi-faceted behaviors. Third, APT-like noise adding. We leverage the forward diffusion model to produce minor and stochastic APT-like noises with efficiency. Fourth, reverse denoising learning. To effectively guide reverse denoising using functionality dependencies, we devise an adaptive contract-level analysis engine equipped with heterogeneous control flow graph modeling and heterogeneous message passing mechanisms to extract function-level and bytecode-level functionality dependencies. Last, contract anomaly detection. We establish a label-efficient attack detector based on reconstruction error for contract anomaly detection. It combines complex dependency analysis and deterministic inference to ensure high-quality data reconstruction and low detection latency. Extensive empirical validations on a manually constructed dataset, covering both mainstream and novel vulnerabilities, demonstrate ConWatcher+'s effectiveness, adaptability, and label efficiency, with an average F1-score of 0.92 across all types of attacks without prior knowledge of corresponding vulnerabilities.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Smart contracts; Blockchains; Adaptation models; Runtime; Logic; Real-time systems; Noise reduction; Costs; Virtual machines; Semantics; Blockchain; smart contract; online contract diagnosis; stealthy contract anomalies; label-efficient detection
【发表时间】2025
【收录时间】2025-09-17
【文献类型】
【DOI】 10.1109/TON.2025.3597004