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2nd International Conference on computer science and Blockchain, CCSB 2022

作者： Cai, Fugang Pan, Heng Si, Xueming Wu, Zhili Qian, Haiyang Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China Zhongyuan University of Technology The Frontier Information Technology Research Institute China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

The lack of centralized management causes problems in the system of distributed parking spaces, such as unsecured revenues, poor service quality and malicious valuation. Therefore, this paper proposes a parking sharing scheme based on non-fungible tokens. Firstly, we propose the calculation method of parking space renter reputation value, parking space provider reputation value and the reputation incentive algorithm of parking space sharing, which can ensure the service quality of distributed parking space share and prevent malicious evaluation and implement bidirectional constraints on parking space provider and renter. Secondly, we propose a parking space sharing model with non-fungible tokens as sharing credentials to map parking space assets to on-chain assets via non-fungible tokens and introduce timers based on hash time locks. We propose an intelligent parking rental contract based on improved hash lock to guarantee the revenue of parking space providers in a distributed environment. Finally, experiments based on Ethereum show that the scheme can realize shared parking space at a lower cost;the proposed incentive algorithm can effectively prevent parking space providers and renters from misbehaving. © 2022 IEEE.

关键词： Smart contract

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LSMM: A Life-cycle security Management Mechanism for Secure Image Sharing 2

LSMM: A Life-cycle Security Management Mechanism for Secure ...

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2nd International Conference on computer science and Blockchain, CCSB 2022

作者： Zhang, Yaoyao Yao, Zhongyuan Pan, Heng Si, Xueming Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China The Frontier Information Technology Research Institute Zhongyuan University of Technology China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

During the storing, transferring, processing, and exchanging phases of the full life-cycle of secure image sharing, there may exist security issues such as information leakage, infringement and abuse. To address those problems, a life-cycle security management mechanism is proposed. By utilizing the Commutative Encryption and Watermarking (CEW), the mechanism ensures that the ownership and confidentiality of one image can hardly be tampered with and compromised. Besides, with the help of the blockchain, the mechanism is enabled to record any changes during the whole process of the image sharing, which makes copyright traceability possible and thus reduces the occurrence of infringement and abuse. The experiment result shows that, in our mechanism, the quality of plaintext image with watermark obtained by decryption is high, and the invisibility of watermark is preserved. Also, the efficient analysis and experimenting of the mechanism exhibits that it is feasible in practice. © 2022 IEEE.

关键词： Life cycle

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FSFuzzer: A High-Performance Greybox Fuzzer for Stateful Network Protocol 1

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4th International Conference on Ubiquitous security, UbiSec 2024

作者： Liu, Qian Zhou, Lei Zhou, Xu Wei, Yuan Liu, Danjun Wu, Zhendong National University of Defense Technology Changsha410000 China National Key Laboratory of Science and Technology on Information System Security Beijing100000 China

ISBN: (数字)9789819648368

ISBN: (纸本)9789819648351

Bugs in network protocol programs exhibit statefulness, making it challenging for fuzzers to generate effective testcases without knowledge of the protocol. Additionally, features such as network transmission and communication synchronization can decrease the execution efficiency of fuzzing in these programs compared to local applications. Current network protocol fuzzing mainly focuses on state inference or execution speed optimization, but these two types of research are orthogonal to each other. However, we observe that enhancing the speed of execution may also increase the power of state inference, leading to improvements in both execution efficiency and testcase generation. In this paper, we introduce FSFuzzer, which leverages shared memory based message transmission and session state sharing based synchronization to alleviate the heavy post-execution analysis in StateAFL state inference. Our experimental results show that FSFuzzer accelerates the execution speed by 4.7x compared to StateAFL. Moreover, we find that the increase in execution speed also boosts StateAFL’s state inference ability, allowing FSFuzzer to infer richer state model information and better guide testcase generation, achieving a notable increase in code coverage and bug-finding capability compared to StateAFL on tested programs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： HTTP

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OpenDrift: Online Evolving Fraud Detection for Open-Category and Concept-Drift Transactions

OpenDrift: Online Evolving Fraud Detection for Open-Category...

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IEEE International Conference on Web Services (ICWS)

作者： Cheng Wang Songyao Chai Hangyu Zhu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges

Unleashing the Potential of Decentralized Federated Learning...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Haibo Cheng Wenjian Liu Youyang Qu Kexue Fu Qinyuan Wang Faculty of Data Science City University of Macau Macau China 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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL’s current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling.

关键词： Surveys Data privacy systematics Federated learning Medical services Data models Servers security Reliability systematic literature review

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Multi-chain Storage Scheme Based on Hash Calendar and Recursive Zero-knowledge Proof 2

Multi-chain Storage Scheme Based on Hash Calendar and Recurs...

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2nd International Conference on computer science and Blockchain, CCSB 2022

作者： Wang, Chao Yao, Zhongyuan Zhang, Yong Guo, Shangkun Zhao, Lindong Si, Xueming Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China Zhongyuan University of Technology The Frontier Information Technology Research Institute China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

Blockchain technology is currently a research hotspot in the computer field. However, with the rapid development of blockchain technology, poor storage scalability limits its further application. In response to this problem, this paper proposes a multi-chain storage scheme based on hash calendar and recursive zero-knowledge proof, which consists of two parts: off-chain and on-chain. The off-chain part uses hash calendar technology to store account information, and through recursive zero-knowledge proof of account state transitions, ensures the verifiability of off-chain state transitions and improves the security of the solution;and sets up proof generators and transactions The two roles of the executor improve the efficiency of transaction execution. A dynamically addable main and sub-chain array is designed on the chain. The sub-chains store the brief information of transactions and the world state. The main chain is responsible for verifying the correctness of off-chain state transitions. On-chain and off-chain collaborative storage reduces the space occupied by data., to realize the storage expansion of the blockchain. Finally, simulation experiments show that this scheme can effectively realize the storage expansion of blockchain. © 2022 IEEE.

关键词： Blockchain

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Interference Coordination technology of Ultra Dense Deployment Based on Clustering Algorithm

Interference Coordination Technology of Ultra Dense Deployme...

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IEEE International Conference on Power, Intelligent Computing and systems (ICPICS)

作者： Haoyuan Wang Yue Tian Wenda Li Fujian Key Laboratory of Communication Network and Information Processing Xiamen University of Technology Xiamen China Department of Security and Crime Science University College London London United Kingdom

A clustering algorithm based Ultra Dense Deployment is proposed to address the problem of establishing a reasonable interference model and allocating ABS reasonably to minimize interference and maximize throughput in Ultra Dense Deployment. By analyzing the distribution pattern of low-power nodes in Ultra Dense Deployment, the interference generated by the cluster center is used to replace the interference of other nodes in the class. According to the principle of interference minimization, a distributed allocation scheme for ABS time slots based on K-means clustering is proposed, and a polling algorithm is used for user scheduling. Simulation results show that compared with the generalized interference model and traditional binary models, the interference calculation is more accurate, The throughput of the system is also higher.

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Robust Beamforming for Downlink Multi-Cell systems: A Bilevel Optimization Perspective

arXiv

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arXiv 2024年

作者： Chen, Xingdi Xiong, Yu Yang, Kai Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming techniques that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study

arXiv

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arXiv 2025年

作者： Liu, Tianhao Liu, Jiqiang Zhang, Tao Wang, Jian Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100045 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication. © 2025, CC BY.

关键词： Generative adversarial networks

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IVSign: Interpretable Vulnerability Signature via Code Embedding and Static Analysis

IVSign: Interpretable Vulnerability Signature via Code Embed...

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International Conference on Dependable systems and Networks Workshops (DSN-W)

作者： Ao Ding Gaolei Li Xiaoyu Yi Yuchen Liu School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security China Department of Computer Science North Carolina State University USA

Software vulnerability detection is evolving from pattern-driven methods to data-driven methods to be more automatic and intelligent due to the emergence of deep learning. However, false positives and coarse-grained detection can not tackle changeable and unknown threats (e.g., Oday vulnerabilities), and also low model interpretability leads to incontinent detection results or maliciously being manipulated. To enhance model confidence and interpretability, in this paper, we propose an Interpretable vulnerability signature (IVSign) scheme to combine pattern-driven and data-driven methods. In IVSign, pattern-driven methods utilize static analysis to provide rich vulnerability trace information extracted from data dependencies and data-driven methods respectively train a transformer-based model to encode these vulnerability traces and a recurrent neural network to generate vulnerability signatures. In the testing stage of IVSign, if a program sample with vulnerabilities is fed into the pipeline, its hit rate in the signature database will be very high. Experiments have shown that when the matching threshold is set appropriately, the evaluation metrics on the testing samples exceed 90.00%.

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