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Enabling Media-Based Modulation for Reconfigurable Intelligent Surface Communications

Enabling Media-Based Modulation for Reconfigurable Intellige...

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IEEE Conference on Wireless Communications and Networking

作者： Yang Yan Yashuai Cao Tiejun Lv Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Reconfigurable intelligent surface (RIS) is highly promising to be applied to enable media-based modulation (MBM) transmissions, which is expected to greatly reduce the hardware cost at the transmitter. In this paper, we propose the two MBM schemes for RIS communications, which are non-differential and differential schemes. In the non-differential scheme, we utilize the phase variations of the RIS to achieve a higher transmission rate compared to the conventional MBM schemes with radio frequency (RF) mirrors. Particularly, in the differential scheme, we decompose the space time block (STB) matrix into a phase state space (PSS) matrix and a selection-permutation (SP) matrix for enhancing the flexibility of differential modulation. The proposed treatment breaks through two inherent design limitations in the traditional differential space time block code (DSTBC). Moreover, the upper bound on the average symbol error rate (SER) of both schemes are derived. Finally, the theory and simulation results show that our proposed schemes can achieve higher transmission rates than the benchmark schemes.

关键词： Radio frequency Upper bound Spectral efficiency Simulation Reconfigurable intelligent surfaces Hardware Matrix decomposition

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Coordinate Attention-Based Convolution Neural Network for In-loop Filter of AVS3

Coordinate Attention-Based Convolution Neural Network for In...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Ruotong Wu Songlin Sun Jiaqi Zou Shaokang Wang Zhilei Ling School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Employing deep learning is a promising solution for reducing encoding bit rate in future video encoding systems. This paper proposes a neural network-based in-loop filter for the third generation of Audio Video Coding Standard (AVS3). The proposed network introduces a coordinate attention mechanism-based convolutional neural network in-loop filter (AMCNNLF) with a flexible attention module and a residual feature aggregation (RFA) module. Specifically, the attention module focuses on salient features to capture the visual structure, while the RFA module takes full advantage of the local refinement features. By leveraging the encoding parameters, we introduce the Quantizer Parameter (QP) values as auxiliary features to enable the proposed network suitable for processing encoded videos with multiple QPs. Experimental results indicate that the proposed network reduces the average Bjøntegaard-Delta rate (BD-Rate) of luma component about 0.4% under all intra configuration compared with the benchmark.

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A Double-Shell Structured Ransomware Defense Method Tailored for the RaaS Model 3rd

A Double-Shell Structured Ransomware Defense Method Tailored...

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3rd International Conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Chen, Monan Ji, Tiantian Li, Shudong Zhang, Yuntao Wang, Tian Mao, Kai Sun, Yanwei Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Peng Cheng Laboratory Shenzhen China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9789819645022

Ransomware attacks have become a significant cybersecurity threat, with the Ransomware as a service (RaaS) paradigm enabling attackers with limited technical expertise to launch sophisticated campaigns. There is currently a lack of systematic research specifically targeting RaaS-type ransomware. This paper analyzes the operational mechanisms of RaaS ransomware, including its industry chain, attack processes, and technical characteristics. We propose a formalized RaaS attack model, providing a clear understanding of its operational methods. To address the limitations of existing defense methods, we introduce an innovative double-shell structured defense model. Utilizing Hook technology and a token verification mechanism, it effectively prevents ransomware from encrypting user data. We demonstrate the effectiveness of this model through experimental validation with a document protection program named RansomShield, achieving a 100% success rate in defending against real ransomware attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computer viruses

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A New Semantic Segmentation Diagram for Intelligent Transportation Based on Heterogeneous Knowledge Base

A New Semantic Segmentation Diagram for Intelligent Transpor...

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IEEE Conference on Wireless Communications and Networking

作者： Jingyuan Tang Jiaqi Zou Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Semantic segmentation is regarded as an important technology for future communication and sensing networks due to its promising ability to extract features of transmit data. It integrates the functionality of computer vision and can realize high-fidelity transmission in the channel with lower bandwidth. Knowledge Base (KB) is a key component for the semantic segmentation framework. In this paper, we consider the scenario of intelligent transportation and propose a heterogeneous KB to extract the to-be-transmitted features and information at multiple levels, i.e., the raw level, symbol level, feature level and image level. The compressed features are obtained by a deep-learning-based framework. The proposed KB is shared with the transmitter and the receiver, by a service-oriented multi-stream transmission algorithm to meet various requirements of services. Experiments results demonstrate significant performance gain in terms of quality of service and encoding efficiency.

关键词： Semantic segmentation Semantics Knowledge based systems Transportation Symbols Object detection Streaming media

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From Passive to Active: Near-optimal DNS-based Data Exfiltration Defense Method Based on Sticky Mechanism

From Passive to Active: Near-optimal DNS-based Data Exfiltra...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Jiawen Diao Binxing Fang Xiang Cui Zhongru Wang Tian Wang Shouyou Song Key Laboratory of Trustworthy Distributed Computing and Service (Beijing University of Posts and Telecommunications) Ministry of Education Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Cyberspace Studies Beijing China Beijing DigApis Technology Co. Ltd Beijing China

DNS-based data exfiltration has become increasingly popular among advanced persistent threat (APT) attackers owing to the ubiquity and penetrability of the DNS protocol. AI-powered methods solve the defect that attackers can easily bypass because of the fixed threshold and weight in rule matching while still suffer from several issues. Such as the lack of malware samples for training, the amplified impact of even low FPR present enormous obstacles to applying the model in real-world *** present a method to generate malicious traffic covering an extensive sample space based on Tactics, Techniques, and Procedures (TTPs). We then propose a sticky mechanism, which transforms certain decision-making into dynamic human-computer interaction decision-making, to verify the suspicious hosts recognized by the AI model. The experimental results demonstrate the superiority of our model by identifying eight kinds of real attacks precisely. The good performance on real-world traffic shows our method is a solid foothold for applying AI-powered detection to practical applications.

关键词： Training Human computer interaction Data privacy Protocols Decision making Transforms Solids

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Chinese Open Relation Extraction with Pointer-Generator Networks

Chinese Open Relation Extraction with Pointer-Generator Netw...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Ziheng Cheng Xu Wu Xiaqing Xie Jingchen Wu Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Most of the traditional Chinese open relation extraction (Open RE) system exploit the syntactic, lexical and other language structure information obtained by natural language processing (NLP) tools from sentences to build hand-crafted patterns for extraction, which is easy to cause error propagation and affect the accuracy of extraction. In this paper, we propose an end-to-end abstract Chinese Open RE model based on the Pointer-Generator network, PGCORE. We employ the results extracted by the state-of-the-art pattern-based Chinese Open RE system as the training set of the model. Experimental results show that our method is outperforms the pattern-based several baselines system, which proves the feasibility and effectiveness of using deep learning models for Chinese Open RE.

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Real-time Event Detection and Tracking in Microblog via Text Chain and Sentiment time series

Real-time Event Detection and Tracking in Microblog via Text...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Bingxu Piao Xu Wu Jingchen Wu Xiaqing Xie Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Microblog becomes one of the most popular media for sharing first-hand information. As more close to real event, detecting and tracking event in microblogs is a research hotspot. To alleviate feature sparsity problem, traditional event detection and tracking methods exploit comments, replies, and reposts to enrich the information of posts. However, ignoring text relationship only leads to more irrelevant information. Aiming at this issue, a novel real-time event detection and tracking method for microblog stream is proposed. For each microblog from the real-time stream, similarity against historical event set is first evaluated. Then, known event microblogs are partitioned by the proposed method sentiment time series, clustered based on word relation graph, and compare with the previous graph to track burst keywords. Meanwhile, text chain is introduced to detect events for unknown event microblogs. Text chain is a unique relationship of microblogs, in which comments, replies, and reposts are used to form a word relation graph of the corresponding post. Posts and attached graphs are clustered to detect new events. Experiments are performed on real-time microblog datasets to evaluate the effectiveness of methods. Results show that the proposed method gains better F-measure than similar methods.

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MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Sy...

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Annual Conference on Privacy, Security and Trust, PST

作者： Panyu Wu Zhenfu Cao Jiachen Shen Xiaolei Dong Yihao Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

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A microservice based generic consensus engine for BaaS

A microservice based generic consensus engine for BaaS

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IEEE International Conference on Industrial Technology (ICIT)

作者： Huiyang Li Xu Zhang Jing Li Dongbin Wang Beijing University of Posts and Telecommunications Beijing China School of Cyber Security National Engineering Laboratory for Mobile Network Security Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing Tongbang Zhuoyi Technology Co. Ltd

ISBN: (纸本)9781728119496

Blockchain-as-a-service (BaaS) has gradually serve as an infrastructure, providing underlying blockchain services for the rapid deployment and transaction on-chain of large-scale enterprise application scenarios. By hosting different blockchains in its underlying structure layers, BaaS platform satisfies the requirements of consensus algorithms in different scenarios, and still uses its own consensus algorithms in different blockchains, which has limited scalability and is difficult to satisfy the requirements of future applications. To deal with such problems, MGCE, a microservice based generic consensus engine for BaaS is proposed in this paper. It is a functional framework based on microservices architecture which can load the appropriate consensus algorithm and perform the consensus process of transactions for blockchain nodes according the different scenarios. The engine is composed of Consensus Node Group, Consensus Management and four functional modules, namely Consensus Network, Transactions Pool, Consensus Protocol and Consensus Outcome. For microservice purpose, each functional module is deployed into a independently project, running in its own process, which is easy to modify and test, and interacts with other modules through the http APIs to complete the consensus process together. To test the function and performance of this engine, three common consensus algorithms have been implemented including Raft, BFT-SMART and PoW. Through experiments, it is proved that this engine could realize the loading and use of different consensus algorithms. Through the performance comparison between using the engine and with the direct use of each consensus algorithm, the results show that the overhead introducing the engine is acceptable. Through the operation result of three algorithms in different projects, the design of engine architecture could satisfy the microservice objective.

关键词： Costs Scalability Loading Microservice architectures Consensus algorithm Consensus protocol Engines

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Linear uncertainty quantification of graphical model inference 24

Linear uncertainty quantification of graphical model inferen...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chenghua Guo Han Yu Jiaxin Liu Chao Chen Qi Li Sihong Xie Xi Zhang Key Laboratory of Trustworthy Distributed Computing and Service (MoE) Beijing University of Posts and Telecommunications China College of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Computer Science and Engineering Lehigh University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) China Department of Computer Science Iowa State University AI Thrust The Hong Kong University of Science and Technology (Guangzhou) China

ISBN: (纸本)9798331314385

Uncertainty Quantification (UQ) is vital for decision makers as it offers insights into the potential reliability of data and model, enabling more informed and risk-aware decision-making. Graphical models, capable of representing data with complex dependencies, are widely used across domains. Existing sampling-based UQ methods are unbiased but cannot guarantee convergence and are time-consuming on large-scale graphs. There are fast UQ methods for graphical models with closed-form solutions and convergence guarantee but with uncertainty underestimation. We propose LinUProp, a UQ method that utilizes a novel linear propagation of uncertainty to model uncertainty among related nodes additively instead of multiplicatively, to offer linear scalability, guaranteed convergence, and closed-form solutions without underestimating uncertainty. Theoretically, we decompose the expected prediction error of the graphical model and prove that the uncertainty computed by LinUProp is the generalized variance component of the decomposition. Experimentally, we demonstrate that LinUProp is consistent with the sampling-based method but with linear scalability and fast convergence. Moreover, LinUProp outperforms competitors in uncertainty-based active learning on four real-world graph datasets, achieving higher accuracy with a lower labeling budget.

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