检索结果-南通市图书馆

Simulation study on the security of consensus algorithms in DAG-based distributed ledger

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Computer Science 2024年第3期18卷 171-182页

作者： Shuzhe LI Hongwei XU Qiong LI Qi HAN Faculty of computing Harbin Institute of TechnologyHarbin 150001China

Due to the advantages of high volume of transactions and low resource consumption,Directed Acyclic Graph(DAG)-based Distributed Ledger Technology(DLT)has been considered a possible next-generation alternative to ***,the security of the DAG-based system has yet to be comprehensively *** at verifying and evaluating the security of DAG-based DLT,we develop a Multi-Agent based IOTA Simulation platform called *** MAIOTASim,we model honest and malicious nodes and simulate the configurable network environment,including network topology and *** double-spending attack is a particular security issue related to *** perform the security verification of the consensus algorithms under multiple double-spending attack *** simulations show that the consensus algorithms can resist the parasite chain attack and partially resist the splitting attack,but they are ineffective under the large weight *** take the cumulative weight difference of transactions as the evaluation criterion and analyze the effect of different consensus algorithms with parameters under each attack ***,MAIOTASim enables users to perform largescale simulations with multiple nodes and tens of thousands of transactions more efficiently than state-of-the-art ones.

关键词： distributed ledger IOTA Multi-Agent system simulation

Enhancing SAEAs with unevaluated solutions:a case study of relation model for expensive optimization

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2024年第2期67卷 41-58页

作者： Hao HAO Xiaoqun ZHANG Aimin ZHOU institute of Natural Sciences Shanghai Jiao Tong University Shanghai Frontiers Science Center of molecule Intelligent Syntheses School of Computer Science and Technology East China Normal University

Surrogate-assisted evolutionary algorithms(SAEAs) hold significant importance in resolving expensive optimization problems. Extensive efforts have been devoted to improving the efficiency of SAEAs through the development of proficient model-assisted solution selection methods, which, however, first requires generating high-quality solutions. The fundamental paradigm of evaluating a limited number of solutions in each generation within SAEAs reduces the variance of adjacent populations, thereby affecting the quality of offspring solutions. This is a frequently encountered issue yet receives little attention. To address the issue, this paper presents a framework using unevaluated solutions to enhance the efficiency of *** surrogate model is employed to identify high-quality solutions for the direct generation of new solutions without evaluation. To ensure dependable selection, we have introduced two tailored relation models to select the optimal solution and the unevaluated population. A comprehensive experimental analysis is performed on two test suites, which showcases the superiority of the relation model over regression and classification models in the solution selection phase. Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to enhance the efficiency of the algorithm significantly.

关键词： expensive optimization unevaluated solutions relation model surrogate-assisted evolutionary algorithm

Porous organic cages as a novel platform for second harmonic generation

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Science China Chemistry 2024年第11期67卷 3669-3674页

作者： Feng Shui Yunqin Zhang Mao Yi Xiongli Liu Lin Li Shuo Zhang Yongshen Zheng Yongtao Yao Qiao Zhao Baiyan Li Xianhe Bu School of Materials Science and Engineering National Institute for Advanced Materials TKL of Metal and Molecule-Based Material Chemistry Nankai University National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of Technology

Second harmonic generation(SHG) is one of the most extensively applied nonlinear optical phenomena. Compared to traditional inorganic SHG materials, organic SHG materials, including non-centrosymmetric small molecular crystals and organic polymers, have been extensively explored for SHG optics due to their advantageous properties, including high light-induced damage threshold, ultrafast response speed, high polarization ratio, wide transparency range, and elevated dielectric constant. However,in organic SHG materials, most small molecular crystals often show poor thermal stability(molecule crystals but also overcome the drawbacks in growing large-size crystals for organic polymer. The obtained two POCs with chiral asymmetric centers(CPOC-R-5 and CPOCS-7) display advantageous features for SHG optics including well SHG performance, large-size crystal(5 mm×3 mm×1 mm),high thermal stability(≥250 °C), wide transparency window(750–2,000 nm) and high polarization ratio(up to 94.5%). This work thus develops POCs as a new platform toward the construction of organic NLO materials with the potential application in sorts of SHG optics.

关键词： porous organic cages second harmonic generation novel SHG platform high thermal stability large-size single crystal

Adaptive Update Distribution Estimation under Probability Byzantine Attack

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials and Continua 2024年第1期81卷 1667-1685页

作者： Long, Gang Zhang, Zhaoxin Faculty of computing Harbin Institute of Technology Harbin 150000 China

The secure and normal operation of distributed networks is crucial for accurate parameter estimation. However, distributed networks are frequently susceptible to Byzantine attacks. Considering real-life scenarios, this paper investigates a probability Byzantine (PB) attack, utilizing a Bernoulli distribution to simulate the attack probability. Historically, additional detection mechanisms are used to mitigate such attacks, leading to increased energy consumption and burdens on distributed nodes, consequently diminishing operational efficiency. Differing from these approaches, an adaptive updating distributed estimation algorithm is proposed to mitigate the impact of PB attacks. In the proposed algorithm, a penalty strategy is initially incorporated during data updates to weaken the influence of the attack. Subsequently, an adaptive fusion weight is employed during data fusion to merge the estimations. Additionally, the reason why this penalty term weakens the attack has been analyzed, and the performance of the proposed algorithm is validated through simulation experiments. © 2024 The Authors. Published by Tech Science Press.

关键词： Data fusion

CCM-FL:Covert communication mechanisms for federated learning in crowd sensing IoT

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Digital Communications and Networks 2024年第3期10卷 597-608页

作者： Hongruo Zhang Yifei Zou Haofei Yin Dongxiao Yu Xiuzhen Cheng institute of Intelligent computing School of Computer Science and TechnologyShandong UniversityQingdao266237China

The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the private information of users in federated learning has become an important research *** with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning *** this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things *** from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal ***,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning *** analysis and nu-merical simulations are presented to show the performance of our covert communication *** hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

关键词： Covert communications Federated learning Crowd sensing SINR model

PUNet: A Semi-Supervised Anomaly Detection Model for Network Anomaly Detection Based on Positive Unlabeled Data

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials and Continua 2024年第1期81卷 327-343页

作者： Long G. Zhang Z. Faculty of computing Harbin Institute of Technology Harbin 150000 China

Network anomaly detection plays a vital role in safeguarding network security. However, the existing network anomaly detection task is typically based on the one-class zero-positive scenario. This approach is susceptible to overfitting during the training process due to discrepancies in data distribution between the training set and the test set. This phenomenon is known as prediction drift. Additionally, the rarity of anomaly data, often masked by normal data, further complicates network anomaly detection. To address these challenges, we propose the PUNet network, which ingeniously combines the strengths of traditional machine learning and deep learning techniques for anomaly detection. Specifically, PUNet employs a reconstruction-based autoencoder to pre-train normal data, enabling the network to capture potential features and correlations within the data. Subsequently, PUNet integrates a sampling algorithm to construct a pseudo-label candidate set among the outliers based on the reconstruction loss of the samples. This approach effectively mitigates the prediction drift problem by incorporating abnormal samples. Furthermore, PUNet utilizes the CatBoost classifier for anomaly detection to tackle potential data imbalance issues within the candidate set. Extensive experimental evaluations demonstrate that PUNet effectively resolves the prediction drift and data imbalance problems, significantly outperforming competing methods. © 2024 The Authors. Published by Tech Science Press.

关键词： candidate set CatBoost Network anomaly detection representation learning

Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第1期33卷 30-42页

作者： Hongning LI Tonghui HU Jiexiong CHEN Xiuqiang WU Qingqi PEI Guangzhou institute of Technology Xidian University Guangzhou CEPREI Shaanxi Key Laboratory of Blockchain and Secure computing

The scarcity of spectrum resources fails to meet the increasing throughput demands of vehicular networks. There is an urgent need to maximize the utilization of spectrum bands in mobile networks. To ascertain the availability of spectrum bands, users should engage in wireless channel sensing and collaboration. However, spectrum sensing data always involves users’ privacy, such as their location. This paper first introduces sensing trajectory inference attack in cognitive vehicular networks and then proposes a data confusion-based privacy-preserving algorithm and a cryptonym array-based privacy-preserving aggregation scheme for spectrum sensing in cognitive vehicular networks. Unlike existing methods, the proposed schemes transmit confused data during the aggregation process. This deliberate obfuscation makes it almost impossible to infer users’ location from the transmitted data. The analysis demonstrates the resilience of the proposed schemes against sensing trajectory inference attack.

关键词： Privacy Data privacy Wireless sensor networks Authentication Data aggregation Throughput Inference algorithms

Extracting Integrated Features of Electronic Medical Records Big Data for Mortality and Phenotype Prediction

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第3期33卷 776-792页

作者： Fei LI Yiqiang CHEN Yang GU Yaowei WANG institute of computing Technology University of Chinese Academy of Sciences Pengcheng Laboratory

The key to synthesizing the features of electronic medical records(EMR) big data and using them for specific medical purposes,such as mortality and phenotype prediction,is to integrate the individual medical event and the overall multivariate time series feature extraction automatically,as well as to alleviate data imbalance *** paper provides a general feature extraction method to reduce manual intervention and automatically process large-scale *** processing uses two variational auto-encoders(VAEs) to automatically extract individual and global *** avoids the well-known posterior collapse problem of Transformer VAE through a uniquely designed "proportional and stabilizing" mechanism and forms a unique means to alleviate the data imbalance *** conducted experiments using ICU-STAY patients' data from the MIMIC-Ⅲ database and compared them with the mainstream EMR time series processing *** results show that the method extracts visible and comprehensive features,alleviates data imbalance problems and improves the accuracy in specific predicting tasks.

关键词： Databases Time series analysis MIMICs Manuals Big Data Feature extraction Transformers

Swarm intention identification via dynamic distribution probability image

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2024年第10期37卷 380-392页

作者： Yinhan WANG Jiang WANG Shaoming HE Fei WANG Qi WANG School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China institute of computing Technology Chinese Academy of SciencesBeijing 100190China

In the realm of decision-making for defense and security applications,it is paramount to swiftly and accurately identify the intentions of incoming *** identification methods predominantly focus on single-target applications and overlook the perturbations introduced by measurement *** this study,we propose a novel concept:the Dynamic Distribution Probability(DDP)image,which is constructed using the estimated state and its covariance *** grayscale pixel value within the image signifies the probability of the presence of the agent within the *** proposed identification scheme integrates the use of Extended Kalman Filter(EKF),Convolutional Neural Network(CNN),Back Propagation(BP)network,and Gated Recurrent Unit(GRU)***,the DDP image is processed through a CNN to distill the formation characteristics,and the estimated swarm state from EKF is inputted into a BP network to deduce the kinematic *** outputs from both networks are summed and subsequently channeled into a GRU network to capture temporal *** numerical simulations and flight experiments are presented to demonstrate the robust anti-noise capability of the proposed scheme compared with conventional methods,as well as its superior training efficiency.

关键词： Intention Identification Kalman filters Convolutional neural networks Swarm intelligence