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A Secure and Reliable Blockchain-based Audit Log System

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A Secure and Reliable Blockchain-based Audit Log System

IEEE International Conference on Communications (ICC)

作者： Zhonghao Liu Xinwei Zhang Guyue Li Helei Cui Jiaheng Wang Bin Xiao Department of Computing The Hong Kong Polytechnic University Hong Kong School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China School of Computer Science Northwestern Polytechnical University Xi'an China National Mobile Communications Reseach Laboratory Southeast University Nanjing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The use of log files in digital forensics highlights the importance of ensuring their data integrity for auditing purposes. However, traditional centralized audit log systems face challenges in maintaining data integrity due to log injection attacks and single-point failures. Although blockchain technology can accurately process and replicate log files, existing blockchain-based audit log systems still suffer from security and reliability issues due to their weak threat models and limited scalability. To address these concerns, we propose a blockchain-based audit log system that ensures data integrity under a general threat model where a part of the nodes, including loggers and auditors, are untrusted. First, our proposed system resists collusion attacks by incorporating multiple nodes for system processes and utilizing smart contracts to enforce consensus algorithms. Second, to save blockchain storage space, we design an efficient log integrity proof method, which generates a sub-Non-Fungible Token (sub-NFT) for each log file and keeps it on the blockchain as integrity proof. The single-point failure problem is resolved by outsourcing log files to a distributed file system. To evaluate the proposed system, we implement a prototype based on Hyperledger Fabric. Experimental results show that our proof generation method can reduce storage space usage in comparison to other blockchain-based audit log systems, saving approximately 50% of space in Hyperledger Fabric. The security analysis proves that our system can ensure log file data integrity under the proposed threat model.

关键词： Threat modeling Distributed ledger Data integrity Scalability Prototypes Fabrics Blockchains

Real-Time Tiny Object Detection and Motion Parameter Extraction of Water Obstacles for Unmanned Surface Vehicle

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SSRN

SSRN 2023年

作者： Zhang, Bin Dong, Wenyong Yan, Shankai Luo, Meizhen Zhao, Chao Wu, Liangshun Peng, Ling School of Remote Sensing and Information Engineering Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Information Network Security Xinjiang University Of Political Science And Law China School of Computer Science Technology Hainan University China Graduate School Binary University of Managment & Entrepreneurship Malaysia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Huanggang Normal University China

The autonomous routing of unmanned surface vehicles relies heavily on the detection of obstacles on the water surface by the sensory module. We know that obstacles on the water have low resolution, blurred pictures, little information and a lot of noise. The detection and recognition of obstacles on the water surface in the autonomous movement of unmanned surface vehicles is always a practical and common difficulty. For the purpose of real-time detection and extraction of movement parameters of obstacles on the water surface, we have made a study to improve the functionality performance of previous perception module by using Kalman filtering algorithms to predicting and updating of motion parameters of obstacle point clouds. We introduced a local relation detector with small target detection algorithm based on attention model at the same *** show that this study can effectively improve the performance of target detection and reduce the *** has made an important contribution to autonomous navigation of unmanned surface vehicles. © 2023, The Authors. All rights reserved.

关键词： Extraction

Empowering Computing and networks Convergence System with Distributed Cooperative Routing

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arXiv 2024年

作者： Hu, Yujiao Jia, Qingmin Shen, Meng Xie, Renchao Huang, Tao Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China Pervasive Communication Research Center Purple Mountain Laboratories Nanjing211111 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Systems and Computer Engineering Carleton University Ottawa Canada

The emergence of intelligent applications and recent advances in the fields of computing and networks are driving the development of computing and networks convergence (CNC) system. However, existing researches failed to achieve comprehensive scheduling optimization of computing and network resources. This shortfall results in some requirements of computing requests unable to be guaranteed in an end-to-end service pattern, negatively impacting the development of CNC systems. In this article, we propose a distributed cooperative routing framework for the CNC system to ensure the deadline requirements and minimize the computation cost of requests. The framework includes trading plane, management plane, control plane and forwarding plane. The cross-plane cooperative end-to-end routing schemes consider both computation efficiency of heterogeneous servers and the network congestion degrees while making routing plan, thereby determining where to execute requests and corresponding routing paths. Simulations results substantiates the performance of our routing schemes in scheduling computing requests in the CNC system. © 2024, CC BY-NC-ND.

关键词： Routing protocols

Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach

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arXiv 2022年

作者： Wang, Yining Chen, Mingzhe Luo, Tao Saad, Walid Niyato, Dusit Poor, H. Vincent Cui, Shuguang Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Wireless VT Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States NTU Singapore Peng Cheng Laboratory Shenzhen China

In this paper, a semantic communication framework is proposed for textual data transmission. In the studied model, a base station (BS) extracts the semantic information from textual data, and transmits it to each user. The semantic information is modeled by a knowledge graph (KG) that consists of a set of semantic triples. After receiving the semantic information, each user recovers the original text using a graph-to-text generation model. To measure the performance of the considered semantic communication framework, a metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed. Due to wireless resource limitations, the BS may not be able to transmit the entire semantic information to each user and satisfy the transmission delay constraint. Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users. As such, we formulate an optimization problem whose goal is to maximize the total MSS by jointly optimizing the resource allocation policy and determining the partial semantic information to be transmitted. To solve this problem, a proximal-policy-optimization-based reinforcement learning (RL) algorithm integrated with an attention network is proposed. The proposed algorithm can evaluate the importance of each triple in the semantic information using an attention network and then, build a relationship between the importance distribution of the triples in the semantic information and the total MSS. Compared to traditional RL algorithms, the proposed algorithm can dynamically adjust its learning rate thus ensuring convergence to a locally optimal solution. Simulation results show that the proposed framework can reduce by 41.3% data that the BS needs to transmit and improve by two-fold the total MSS compared to a standard communication network without using semantic communication techniques. Copyright © 2022, The A

关键词： Reinforcement learning

A data-driven fault diagnosis method for photovoltaic modules

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A data-driven fault diagnosis method for photovoltaic module...

IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Yang Jiurong Sun Xingjian Ma Zhuoran Han Xiaojuan Safety and Quality Department China Electric Power Research Institute Co. Ltd Beijing School of Control and Computer Engineering North China Electric Power University Beijing Intelligent Distribution Network Center State Grid Jibei Electric Power Co. Ltd

The existing fault diagnosis of photovoltaic modules mainly focuses on a single fault, while in actual operation, the impact of climate and environmental conditions often leads to multiple faults occurring simultaneously. In response to the above issues, a data-driven composite fault diagnosis method for photovoltaic modules is proposed in this paper. The simulation model of Photovoltaic system is built, and the fault characteristic parameters of photovoltaic modules under different states are extracted according to the output characteristic curves under the three states of normal, single fault and composite fault. The composite fault diagnosis model of photovoltaic modules based on hybrid kernel Extreme learning machine is established. The parameters of hybrid kernel Extreme learning machine are optimized by using adaptive strategy to improve sparrow search algorithm, The accuracy of diagnosis model of hybrid kernel Extreme learning machine is improved. The effectiveness of the proposed method is verified through a simulation example of the actual operation data of a photovoltaic power plant. The simulation results show that this method can accurately diagnose single and composite faults of photovoltaic modules, and has certain engineering application prospects.

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The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined network (SDN) and network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： network security

Semantic Communications with Explicit Semantic Base for Image Transmission

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arXiv 2023年

作者： Zheng, Yuan Wang, Fengyu Xu, Wenjun Pan, Miao Zhang, Ping State Key Laboratory of Network and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

Semantic communications, aiming at ensuring the successful delivery of the meaning of information, are expected to be one of the potential techniques for the next generation communications. However, the knowledge forming and synchronizing mechanism that enables semantic communication systems to extract and interpret the semantics of information according to the communication intents is still immature. In this paper, we propose a semantic image transmission framework with explicit semantic base (Seb), where Sebs are generated and employed as the knowledge shared between the transmitter and the receiver with flexible granularity. To represent images with Sebs, a novel Seb-based reference image generator is proposed to generate Sebs and then decompose the transmitted images. To further encode/decode the residual information for precise image reconstruction, a Seb-based image encoder/decoder is proposed. The key components of the proposed framework are optimized jointly by end-to-end (E2E) training, where the loss function is dedicatedly designed to tackle the problem of non-differentiable operation in Seb-based reference image generator by introducing a gradient approximation mechanism. Extensive experiments show that the proposed framework outperforms state-of-art works by 0.5 − 1.5 dB in peak signal-to-noise ratio (PSNR) w.r.t. different signal-to-noise ratios (SNR). Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

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computers, Materials & Continua 2020年第1期62卷 293-307页

作者： Huanrong Tang Aoming Peng Dongming Zhang Tianming Liu Jianquan Ouyang Key Laboratory of Intelligent Computing and Information Processing Ministry of EducationCollege of Information EngineeringXiangtan UniversityXiangtan411105China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029China Department of Computer Science the University of GeorgiaAthensGeorgiaUSA

With the improvement of the national economic level,the number of vehicles is still increasing year by *** to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is *** parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much *** to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and ***,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified ***,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training ***,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking.

关键词： Contextual information transmission illegal parking detection spatial attention mechanism deep learning

TASS: Threshold Adaptor Signature Scheme for Holographic IoT Consumer Devices in Blockchain Enabled Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Zengxiang Qin, Zhen Kumari, Saru Feng, Chaosheng Alenazi, Mohammed J.F Wu, Deyan Chen, Chien-Ming Xiong, Hu University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Sichuan Chengdu610054 China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut India Sichuan Normal University School of Computer Science Sichuan Chengdu610101 China Department of Computer Engineering Riyadh11451 Saudi Arabia Sichuan Institute of Computer Sciences China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing250004 China

The integration of virtual medical assistants utilizing holographic projections has transformed holographic IoT consumer devices, fostering advanced healthcare interactions and attracting significant interest from industry and academia. Most federated learning (FL) systems in holographic IoT consumer devices are centrally managed, which exposes them to risks such as medical data breaches and single points of failure. Although blockchain technology provides an effective solution by enabling decentralized model training, it faces inherent limitations in terms of computational efficiency and security. Adapter signatures (AS) offload some of the computation on the blockchain to mitigate these problems through means such as key revelation. Despite their advantages, existing AS schemes suffer from bilateral design constraints, which increase the risks of key loss, single points of failure, and lack support for multi-party transaction scenarios. This paper proposes a novel threshold adapter signature scheme (TASS) based on elliptic curve cryptography (ECC) to address these challenges. TASS allows a group of entities to collaboratively generate partial signatures in a distributed manner, reducing the risks associated with key loss and single points of failure. Each participant holds only a share of the private key, ensuring that no single entity has complete control. This distributed approach enhances the system’s resilience against attacks and operational failures. Unlike traditional AS schemes that primarily support one-to-one transactions, TASS is designed to facilitate one-to-many and many-to-many transactions. Experimental evaluations demonstrate the superior performance of ECC-driven TASS in large-scale participants, providing a secure, efficient, and scalable foundation for integrating holographic interactions and blockchain technology within holographic IoT consumer devices. © 1975-2011 IEEE.

关键词： Privacy by design