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A software upgrade security analysis method on network traffic classification using deep learning

A software upgrade security analysis method on network traff...

引用

2020 International Conference on Urban engineering and Management Science, ICUEMS 2020

作者： Zhang, Bing State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

ISBN: (纸本)9781728188324

Nowadays, the vulnerability in the software upgrade process are extremely harmful to network security. However, the detection of upgrade vulnerability is facing serious difficulties and problems. Aiming at the current software upgrade vulnerability analysis problem, we propose a neural network model for traffic classification based on two levels of data packets and network flows. This method does not need to manually extract features and can learn the dependency relationship between data packets and networks well. It can make full use of comprehensive flow features for traffic classification. In order to evaluate the model, we built a dataset of software upgrade vulnerabilities and conducted relevant experiments on the CICIDS 2017 dataset. The experimental results show that compared with other RNN models, our model has the best performance, reaching a precision of 99.86% and an F1 score of 99.44%. At the same time, based on the pre-trained model, we use the software upgrade vulnerability dataset to fine-tuning the model, with the F1 score reaching 99.78%. © 2020 IEEE.

关键词： Neural network models

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Brain MR Images Super-Resolution with the Consistent Features 2022

Brain MR Images Super-Resolution with the Consistent Feature...

引用

14th International Conference on Machine Learning and computing, ICMLC 2022

作者： You, Senrong Shen, Yanyan Wu, Guocheng Wang, Shuqiang Center for Biomedical Information Technology Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Center for Internet of Things Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Data Recovery Key Laboratory of Sichuan Province College of Mathematics and Information Science Neijiang Normal University China

ISBN: (纸本)9781450395700

Magnetic resonance imaging plays an important role in auxiliary diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, consistent feature generative adversarial network (CFGAN) is proposed to produce HR MR images from the low-resolution counterparts. Specifically, a consistent-features encoder is employed to extract the multi-scales features and encode them into latent codes. Then, a progressive generator is utilized to decode the latent codes from high-level to low-level features. With the encoder and generator, the shared consistent features between low-resolution and high-resolution can be fully extracted and recovered. Experiments on ADNI dataset demonstrate that CFGAN outperforms the competing methods quantitatively and qualitatively. © 2022 Owner/Author.

关键词： Generative adversarial networks

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Distributed Deep Learning System for Efficient Face Recognition in Surveillance System 21

Distributed Deep Learning System for Efficient Face Recognit...

引用

Proceedings of the 3rd International Conference on advanced Information Science and System

作者： Jinjin Liu Zhifeng Chen Xiaonan Li Tongxin Wei School of Computer Science Zhongyuan University of Technology China and State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Mathematical Engineering and Advanced Computing China School of Computer Science Zhongyuan University of Technology China School of Cyberspace Security Zhengzhou University China

ISBN: (纸本)9781450385862

In view of the bandwidth consumption caused by data stream transmission in video analysis system and the demand for accurate online real-time analysis of massive data, this paper proposes a deep learning model framework for face recognition employed in the embedded system. Through data collaboration, the cloud could build a more complex data set with a small amount of uploaded data gathered by the end devices. And the framework collaboration makes sure that the fully-trained cloud model directly download or distillate knowledge to the end devices. Experiments show that the deep model not only realizes the real-time response and the accurate response of the cloud system, but also greatly reduces the bandwidth consumption caused by sample data transmission in the model training process.

关键词： Deep learning

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The Executors Scheduling Algorithm for the Web Server Based on the Attack Surface

The Executors Scheduling Algorithm for the Web Server Based ...

引用

2020 IEEE International Conference on Advances in Electrical engineering and Computer Applications, AEECA 2020

作者： Nie, Guanglai Zhang, Zheng Zhao, Yufeng Meac State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728165202

In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm. © 2020 IEEE.

关键词： Scheduling algorithms

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Hot Spot Locating and Optimization of Calculation Errors in Arithmetic Expressions 7

Hot Spot Locating and Optimization of Calculation Errors in ...

引用

7th International Symposium on Advances in Electrical, Electronics, and Computer engineering

作者： Jinchen, Xu Yuanyuan, Xia Jiangwei, Hao Hao, Cao State Key Laboratory of Mathematical Engineering and Advanced Computing No. 62 Science Avenue High-Tech Zone Henan Zhengzhou450001 China National Key Laboratory of Science and Technology on Integrated Information Systems Technology Institute of Software Chinese Academy of Sciences No. 4 Zhongguancun South Fourth Street Haidian District Beijing10190 China

ISBN: (纸本)9781510656437

Error analysis is an important support for improving the precision of floating-point calculations. However, the floating-point error distribution does not have dominant characteristics, making it difficult to optimize floating-point calculation precision. This paper proposes the LFEE (Locating hot spots of floating-point expression errors) algorithm. The algorithm analyzes the error detection result of the expression to obtain the error hot spot, i.e., the step with the maximum error in the expression calculation sequence, followed by preforming precision optimization scheme for the error hot spot. The experimental results show that the LFEE algorithm can accurately locate the error hot spots and improve the accuracy of the function by optimizing the error hot spots. © 2022 SPIE. All rights reserved.

关键词： Error detection

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Cluster-Based Routing Algorithm for WSN Based on Subtractive Clustering 16

Cluster-Based Routing Algorithm for WSN Based on Subtractive...

引用

16th IEEE International Wireless Communications and Mobile computing Conference, IWCMC 2020

作者： Chen, Ling Liu, Wenwen Gong, Daofu Chen, Yan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (纸本)9781728131290

The Cluster-based Routing Algorithm for Wireless Sensor Network based on Subtractive Clustering (SCC algorithm) generates nodes of cluster head in dense area of the node by relying on subtractive clustering. The application of this algorithm effectively solves the attribution problem of non-cluster head node in conventional algorithms, and consumption is evenly distributed throughout the network. Through simulation experiments, it can be found that the application of the SCC algorithm can map the distribution of nodes of cluster head reasonably and delay the death time of the first node to prolong the lifetime of network and balance the energy consumption of nodes. © 2020 IEEE.

关键词： Routing algorithms

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Noise Detection and Removal for Underground Communication Systems Based on L2 N onnal Inner Product

Noise Detection and Removal for Underground Communication Sy...

引用

IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： Bangwei Yu Yong Wang Liangang Qi Yang Liu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Sichuan Key Laboratory of Agile Intelligent Computing Chengdu China

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In underground communication systems, continuous mud pulse signals are susceptible to pump noise during transmission, resulting in a high bit error rate(BER). In this paper, a Normal Inner Product Orthogonal Matching Pursuit(NIPOMP) algorithm is proposed for the transmission characteristics of continuous waves in the underground. First, a cyclic prefix(CP) is added in front of the sent data. Then the observation vector is obtained by making a difference between the CP of the latter set of data and the CP of the former set of data. Then, the columns of the sensing matrix that are most correlated with the observation vectors are selected as candidate support sets by calculating the L2 norm. The least squares method was used to solve for the previous set of pump noise estimates. Finally, the pump noise is reconstructed using the correspondence between the time and frequency domains. This paper establishes a complete underground communication system. We discuss the noise characteristics of pump noise and analyze the denoising performance of the NIPOMP algorithm in depth. Simulation results show that the algorithm significantly reduces the system BER in complex channel environments with more drastic fading, including gas-phase, liquid-phase, gas-liquid two-phase, and liquid-solid two-channel environments.

关键词： Liquids Communication systems Simulation Oils Noise Noise reduction Matching pursuit algorithms Vectors Sensors Signal to noise ratio

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An evaluation algorithm of the importance of network node based on community influence 1

引用

5th International Conference on Data Mining and Big Data, DMBD 2020

作者： He, Gongzhen Luo, Junyong Yin, Meijuan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (数字)9789811572050

ISBN: (纸本)9789811572043

Identifying nodes in social networks that have great influence on information dissemination is of great significance for monitoring and guiding information dissemination. There are few methods to study the influence of communities on social networks among the existing node importance evaluation algorithms, and it is difficult to find nodes that promote information dissemination among communities. In view of this reason, this paper proposes a node importance evaluation algorithm based on community influence (abbreviated as IEBoCI algorithm), which evaluates the importance of the nodes based on the influence degree of the nodes on the communities and the ability to disseminate information the communities to which the nodes are connected. This algorithm firstly calculates the activation probability of nodes to other nodes, which is used to divide communities and evaluate influence. Secondly, the network is divided into communities based on LPA algorithm. Finally, the importance of the node is calculated by combining the influence of the community itself and the influence of the node on the community. Experiments are carried out on real social network data and compared with other community-based methods to verify the effectiveness of the algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Complex networks

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Network representation learning based extended matrix factorization for recommendation 6th

Network representation learning based extended matrix factor...

引用

6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Xu, Jinmao Gong, Daofu Liu, Fenlin Tan, Lei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811581007

The growing heterogeneous data in the Internet effectively improves the performance of recommender systems (RS). The main problem faced by the traditional matrix factorization (MF) is how to fuse more heterogeneous information data in MF to improve the performance of RS. In view of this, this paper proposes an Extended Matrix Factorization (EMF) based on network representation learning. EMF integrates multiple types of data in Heterogeneous Information Network (HIN) to improve the accuracy of prediction rating. The nodes in HIN are first mapped to a low-dimensional representation vector by network representation learning (NRL). Then the representation vector is used as the input of the EMF, the parameters are optimized by the gradient descent, and finally the prediction model is obtained. The experiments on two real data sets show the effectiveness of the EMF. Compared with the baseline algorithms, the EMF model can obtain more accurate prediction rating. © Springer Nature Singapore Pte Ltd 2020.

关键词： Recommender systems

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Research on the Protection Mechanism of Cisco IOS Exploit 4

Research on the Protection Mechanism of Cisco IOS Exploit

引用

4th International Conference on Data Mining, Communications and Information Technology, DMCIT 2020

作者： Wang, Jinzhu Cai, Ruijie Liu, Shengli State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

Cisco IOS is an operating system that runs on Cisco routing and switching equipment. Its security is important to the entire network. Since vulnerability attacks have become one of the main sources of threat to Cisco IOS security, it is necessary to analyze the defense mechanism of IOS against vulnerability attacks. The purpose of this paper is to find out the deficiencies of IOS vulnerability defense mechanism in time and put forward improvement methods and preventive measures. Firstly, this paper introduces the protection mechanism of Cisco IOS vulnerability, including CheckHeaps, DEP, ASLR. The principle of these mechanisms and the idea of vulnerability protection are analyzed. And then, after analyzing the shortcomings of these protection mechanisms, the basic idea of bypassing these protection mechanisms is proposed. Finally, in view of the shortcomings of Cisco IOS vulnerability defense mechanism and the proposed bypass methods, some advices about how to better protect Cisco IOS from vulnerability exploitation are given. © Published under licence by IOP Publishing Ltd.

关键词： Network security

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