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A Blockchain-based Revocable Certificateless Signature Scheme for IoT Device

International Journal of Network security 2021年第6期23卷 1012-1027页

作者： Chen, Yushuang Zheng, Dong Guo, Rui Zhang, Yinghui Tao, Xiaoling School of Cyberspace Security Xi’an University of Posts and Telecommunications Xi’an710121 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China National Engineering Laboratory for Wireless Security Xi’an University of Posts and Telecommunications Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

With the rapid development of Internet of Things (IoT) technology, completing a transaction with intelligent devices has been an essential communication mode in our daily life. Generally, lightweight smart devices conduct transactions relying on the third-party payment platform, which takes several inherent challenges of mutual distrust, data authentication and low efficiency in the IoT environment. To mitigate these issues, in this paper, we design a paring-free revocable certificateless signature (CLS) scheme with high efficiency based on the blockchain technique. Our scheme is a viable approach to enhance the performance without bilinear parings operation in the IoT lightweight equipment. And there are two different address lists stored in the blockchain to record the secret and public transaction data, respectively. According to this method, not only can the key management of the user be implemented in an efficient solution, but also enables the public verification to be performed transparently between the two parties. Furthermore, with the help of cloud server, it generates a time key for the user to accomplish revocation with low computation cost. We also make an analysis on the security to demonstrate that our proposal is existential unforgeability against adaptive chosen message attacks (EUF-CMA) under the elliptic curve discrete logarithm (ECDL) problem. Finally, the performance comparison indicates that our protocol is better than other related schemes. © 2021. International Journal of Network security. All Rights Reserved.

关键词： Blockchain

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An improved method of cache prefetching for small files in ceph system 5th

An improved method of cache prefetching for small files in c...

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5th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2018

作者： Fan, Ya Wang, Yong Ye, Miao Lu, Xiaoxia Huan, YiMing School of Computer Science and Information Security Guilin University of Electronic Technology GuilinGuangxi541004 China School of Information and Communication Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology GuilinGuangxi541004 China

ISBN: (纸本)9783030037659

To improve the inefficiency of file access for massive small files in the distributed file system, prefetching the small files into the cache is the most conventional method to be adopted. As the number of cached files is proportional to time, there will be many redundant small files which occupy the cache space and haven’t been read for a long time, the hit ratio will be decreased in this situation. To solve this defect, we proposed an improved LRU-W algorithm based on the file read times and the time interval of file read and designed a L2 cache optimization mechanism which can search file in the linked list with higher priority firstly and remove the files with lighter weight factor from the linked list with lower priority dynamically. The experiments and its result show that when prefetching massive small files, the proposed method in this paper can increase the hit ratio of cached files and improve the overall performance of Ceph file system. © Springer Nature Switzerland AG 2019.

关键词： File organization

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Publication recommendations of manuscripts using improved C4.5 decision tree algorithm 2nd

Publication recommendations of manuscripts using improved C4...

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2nd International Conference on Intelligent and Interactive Systems and Applications, IISA 2017

作者： Jia, Didi Xie, Wu Chen, Zhiyong Qiang, Baohua School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin541004 China

ISBN: (纸本)9783319690957

In view of the problem that the result accuracy of the research publication recommendations of manuscripts is often low. It is very difficult for many contributors to find the suitable journals from many recommendation results. To solve this problem, a novel method of publication recommendations for authors’ manuscripts is proposed using our improved C4.5 decision tree algorithm. For the shortcomings of too many values of data samples during dealing with the traditional C4.5 methods, the five-value logic ideas are adopted to improve this C4.5 algorithm to apply in the research fields of publication recommendations. The experimental results show that the related publications are easier to obtain for authors via the decision trees with the improved C4.5 method than before, and this method has higher accuracy than ever. It is of great significance to help the researchers, especially for those who have no enough experience to choose the most suitable journals from a lot of publications. © 2018, Springer International Publishing AG.

关键词： Decision trees

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Multi-criteria collaborative filtering based on matrix filling 4

Multi-criteria collaborative filtering based on matrix filli...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Ye, Li Wu, Chunming Qiang, Baohua Xie, Wu College of Computer Information Science Southwest University Chongqing China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781538683392

In multi-criteria collaborative filtering, the data sparsity is a critical factor affecting the effectiveness of the algorithm. Existing solutions generally remove low-efficiency data through dimensionality reduction, decomposition, etc. While at lower data dimension, they also lose original data information, which impacts the accuracy of predicted ratings. This paper adopts a new processing strategy and proposes to use the matrix filling method to handle the missing data. Firstly, an improved similarity algorithm combined with the Jaccard Similarity Coefficient is used to calculate the predicted rating. Secondly, the Reliable Factor is introduced to prevent the error of filling data. Finally, multiple linear regression is employed for aggregating the ratings of the criteria to eventually yield preferred predicted ratings. A series of experiments show that the proposed method can precisely reflect the user's similarity and achieve better results in predicted ratings, in contrast to traditional methods. © 2018 IEEE.

关键词： Collaborative filtering

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Network Traffic Outlier Detection Based on Full Convolutional Network

Network Traffic Outlier Detection Based on Full Convolutiona...

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International Conference on Computational Intelligence and security

作者： Jingrong Mo Yong Wang Miao Ye School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin Guangxi China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin Guangxi China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9781728160924

ISBN: (纸本)9781728160931

Network traffic flow can be represented as time series. Outliers of these time series is important for the computer network security. The existing methods for the time series anomaly detection mainly operate on the entire time series to detect the abnormal series, seldom find out the position of the abnormal point. To solve this defect, we propose a method to discover the location of the outliers in time series based on a full convolutional network. We first map the time series into two-dimensional grayscale images by sliding window approach, and then feed them into the full convolutional network. In the classified stage, the feature maps of the last convolution layer are restored to the same size as the input image by up-sampling. Finally, we predict the class for each pixel and calculate the loss on a pixel-by-pixel basis. We tested the model on Yahoo Webscope S5 anomaly benchmark dataset, and the results show that our proposed method can achieve a mean pixel accuracy of 99.4% and mean IOU of 97% on the test dataset.

关键词： Time series analysis Telecommunication traffic Anomaly detection Convolution Feature extraction Gray-scale Machine learning

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A New Construction Method of Regeneration Tree for Single Node Fault Repair Mechanism in Distributed Storage System

A New Construction Method of Regeneration Tree for Single No...

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International Conference on Computational Intelligence and security

作者： Wei Guo Yong Wang Miao Ye School of Computer Science and Information Security Guilin University of Electronic Technoloay Guilin Guangxi China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin Guangxi China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin Guangxi China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9781728160924

ISBN: (纸本)9781728160931

Comparing with the classic redundancy policy of multi-replica technology, Erasure code is preferred due to its higher utilization rate of storage space in distributed storage system. As one kind of the important Erasure code, the traditional regeneration code with star recovery topology cost more time and more network bandwidth in the data recovery process. An efficient approach to reduce the delay time and network consumption is to construct an optimal recovery tree with the best possible bottleneck bandwidth, which is proved to be a Non-deterministic Polynomial problem. To solve this problem, this paper proposed a hybrid genetic algorithm which utilizes the designed crossover operation and mutation operation according to the problem property. A series of experiments have been conducted and the results show that our proposed method has good convergent ability and reduce the regeneration time.

关键词： Topology Bandwidth Genetic algorithms Encoding Network topology Sociology Statistics

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Real-time network traffic classification based on CDH pattern matching 14

Real-time network traffic classification based on CDH patter...

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14th International Conference on Computational Intelligence and security, CIS 2018

作者： Li, Xunzhang Wang, Yong Ke, Wenlong Feng, Hao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi541004 China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin Guangxi541004 China

ISBN: (纸本)9781728101699

In recent years, with the rapid development of the Internet, the data scale of application behavior and application traffic have exploded. How to classify the real-time traffic of network becomes a big challenge. How to balance the accuracy and real-time of traffic classification is a difficult problem in technology. Therefore, this paper proposes a pattern matching real-time traffic classification method named PM, which first uses jpcap to accept network traffic data in real time, and then uses pattern matching to perform real-time matching traffic characteristics to achieve traffic classification. Among them, the use of the distributed message system kafka and the parallel computing framework Spark significantly improve the execution efficiency of the program. The experimental results show that PM has good performance in terms of accuracy. © 2018 IEEE.

关键词： Pattern matching

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Xiong Tianmin Lei Xiaochun Zheng Xingwen Chen Junyan Feng Yizhou School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields.

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Design of Intelligent Robot Platform based on Multi-sensor Fusion

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Ya Dajin Lei Xiaochun Li Yue Chen Junyan Huang Rongcun Lan Lin School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor Information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system.

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Multi-criteria Collaborative Filtering Based on Matrix Filling

Multi-criteria Collaborative Filtering Based on Matrix Filli...

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International Conference on Computer and Communications (ICCC)

作者： Li Ye Chunming Wu Baohua Qiang Wu Xie College of Computer & Information Science Southwest University Chongqing China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781538683408

关键词： Filling Collaboration Filtering Reliability Prediction algorithms Linear regression Dimensionality reduction

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