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2nd International Conference on Computer network, Electronic and Automation, ICCNEA 2019

作者： Tianma, Wang Wang, Fangwei Stuart School of Business Illinois Institute of Technology Chicago United States College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Key Laboratory of Network and Information Security Hebei Province Shijiazhuang China

ISBN: (纸本)0769566847

This paper uses SPSS tools and factor analysis theory to make a comprehensive evaluation of the company's stock performance. Data come from 10 representative listed companies in different industries. Because of the high weight and strong correlation of the original data, it is impossible to obtain the original data directly. Therefore, it is necessary to reduce the number of variables, which not only completes the dimension reduction statistics, but also doesn't cause a large number of data loss, so that the factors of reorganization of the original variables ensure the weak linear relationship between them. Through data standardization, factor analysis and factor analysis, factor analysis model is obtained. From the final model, we can also see that in the comprehensive evaluation model, the proportion of comprehensive factors of enterprise profitability is the highest, and the proportion of comprehensive factors reflecting inventory turnover rate is the smallest, but the proportion of the four comprehensive factors is not much different. On the basis of practical analysis, the conclusion is reasonable, and the company's performance can be seen from the point of score. © 2019 IEEE.

关键词： Machine learning

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Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning

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Journal of Computer Science & technology 2019年第5期34卷 1039-1062页

作者： Zhou Xu Shuai Pang Tao Zhang Xia-Pu Luo Jin Liu Yu-Tian Tang Xiao Yu Lei Xue College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science Wuhan UniversityWuhan 430072China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong 999077China Key Laboratory of Network Assessment Technology Institute of Information EngineeringChinese Academy of Sciences Beijing 100190China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Department of Computer Science City University of Hong KongHong Kong 999077China

Defect prediction assists the rational allocation of testing resources by detecting the potentially defective software modules before releasing products. When a project has no historical labeled defect data, cross project defect prediction (CPDP) is an alternative technique for this scenario. CPDP utilizes labeled defect data of an external project to construct a classification model to predict the module labels of the current project. Transfer learning based CPDP methods are the current mainstream. In general, such methods aim to minimize the distribution differences between the data of the two projects. However, previous methods mainly focus on the marginal distribution difference but ignore the conditional distribution difference, which will lead to unsatisfactory performance. In this work, we use a novel balanced distribution adaptation (BDA) based transfer learning method to narrow this gap. BDA simultaneously considers the two kinds of distribution differences and adaptively assigns different weights to them. To evaluate the effectiveness of BDA for CPDP performance, we conduct experiments on 18 projects from four datasets using six indicators (i.e., F-measure, g-means, Balance, AUC, EARecall, and EAF-measure). Compared with 12 baseline methods, BDA achieves average improvements of 23.8%, 12.5%, 11.5%, 4.7%, 34.2%, and 33.7% in terms of the six indicators respectively over four datasets.

关键词： cross-project defect prediction transfer learning balancing distribution effort-aware indicator

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Label-consistency based graph neural networks for semi-supervised node classification

arXiv

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arXiv 2020年

作者： Xu, Bingbing Huang, Junjie Hou, Liang Shen, Huawei Gao, Jinhua Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences China

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. In this paper, we propose label-consistency based graph neural network (LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification. We further show the superiority of LC-GNN in sparse scenarios with only a handful of labeled nodes. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Enhancing security of the Reduced-Operation Two-Factor Authentication by Using Ambient WiFi

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Chinese Journal of Electronics 2018年第3期27卷 625-633页

作者： LIU Dong CHEN Jing DU Ruiying ZHANG Huanguo Key Laboratory of Aerospace Information Security and the Trusted Computing Ministry of Education School of ComputerWuhan University Science and Technology on Communication Security Laboratory North Automatic Control Technique Research Institute Collaborative Innovation Center of Geospatial Technology

Recently several reduced-operation two Factor authentication(2FA) methods have been proposed to improve the usability of traditional 2FA. The existing works cannot protect the user's password from online guessing attack and offline dictionary attack. They cannot resist the identity fraud attack caused by co-located attackers who have obtained the victim's password. To solve these problems, we provide a Wi Fi-based 2FA approach which can enhance security of the reduced-operation 2FA but not increase the complexity of the operation for a *** analyze our approach's security in terms of identity fraud attack resistance, salt guessing attack resistance, and password guessing attack resistance. We also implement a prototype system and test its performance in various scenarios, e.g. lab, library, and dormitory. The security analysis and experimental results show the effectiveness of our scheme for authentication.

关键词： Two factor authentication(2FA) Proximate authentication Fuzzy vault

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Improving fast segmentation with teacher-student learning 29

Improving fast segmentation with teacher-student learning

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29th British Machine Vision Conference, BMVC 2018

作者： Xie, Jiafeng Shuai, Bing Hu, Jian-Fang Lin, Jingyang Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Nanyang Technological University Singapore Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing MOE Hong Kong

Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks. However, these models are very heavy and generally suffer from low inference speed, which limits their application scenarios in practice. Meanwhile, existing fast segmentation models usually fail to obtain satisfactory segmentation accuracies on public benchmarks. In this paper, we propose a teacher-student learning framework that transfers the knowledge gained by a heavy and better performed segmentation network (i.e. teacher) to guide the learning of fast segmentation networks (i.e. student). Specifically, both zero-order and first-order knowledge depicted in the fine annotated images and unlabeled auxiliary data are transferred to regularize our student learning. The proposed method can improve existing fast segmentation models without incurring extra computational overhead, so it can still process images with the same fast speed. Extensive experiments on the Pascal Context, Cityscape and VOC 2012 datasets demonstrate that the proposed teacher-student learning framework is able to significantly boost the performance of student network. © 2018. The copyright of this document resides with its authors.

关键词： Students

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CarfRCNN: A Two-stage Effective Model For Instance Segmentation

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Journal of Physics: Conference Series 2021年第1期1884卷

作者： Yujie Chen Pengbiao Zhao Julin Chen Network and Data Security Key Laboratory of Sichuan Province University of Electr onic Science and Technology of China

With the rapid development of deep learning, many instance segmentation models have achieved good results in accuracy and time. But here are still many problems. In this paper, we proposed a two-stage model CarfRCNN. We proposed CAResNet to change the structure of the backbone, making the feature extraction of the input image more refined. At the same time, we also added the CRF module to add smooth constraints to the pixels, so that the segmentation mask fits the target contour more closely. We train and test on the COCO datasets, and compare with the Mask R-CNN. The experimental results prove that the model we proposed can greatly improve the accuracy.

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A security scheme for intelligent substation communications considering real-time performance

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Journal of Modern Power Systems and Clean Energy 2019年第4期7卷 948-961页

作者： Jie ZHANG Jun’e LI Xiong CHEN Ming NI Ting WANG Jianbo LUO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072China NARI Group Corporation(State Grid Electric Power Research Institute) Nanjing 211106China NARI Technology Co.Ltd. Nanjing 211106China State Key Laboratory of Smart Grid Protection and Control Nanjing 211106China

Tampering,forgery and theft of the measurement and control messages in a smart grid could cause one breakdown in the power ***,no security measures are employed for communications in intelligent *** services in an intelligent substation have high demands for real-time performance,which must be considered when deploying security *** paper studies the security requirements of communication services in intelligent substations,analyzes the security capabilities and shortages of IEC 62351,and proposes a novel security scheme for intelligent substation *** security scheme covers internal and telecontrol communications,in which the real-time performance of each security measure is *** this scheme,certificateless public key cryptography(CLPKC)is used to avoid the latency of certificate exchange in certificate-based cryptosystem and the problem of key escrow in identity-based cryptosystem;the security measures of generic object-oriented substation event,sampled measure value and manufacturing message specification in IEC 62351 are improved to meet the real-time requirements of the messages as well as to provide new security features to resist repudiation and replay attacks;and the security at transport layer is modified to fit CLPKC,which implements mutual authentication by exchanging ***,a deployment of CLPKC in an intelligent substation is *** also evaluate the security properties of the scheme and analyze the end-to-end delays of secured services by combining theoretical calculation and simulation in this *** results indicate that the proposed scheme meets the requirements of security and real-time performance of communications in intelligent substations.

关键词： Intelligent substation security measures Certificateless public key cryptography(CLPKC) Real-time communication IEC 62351

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DLEDNet: A deep learning-based image encryption and decryption network for internet of medical things

arXiv

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arXiv 2020年

作者： Ding, Yi Wu, Guozheng Chen, Dajiang Zhang, Ning Gong, Linpeng Cao, Mingsheng Qin, Zhiguang Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongwan Guangdong China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China National Natural Science Foundation of China Beijing China Department of Computing Science Texas A&M University-Corpus Christi Corpus ChristiTX78412 United States

With the rapid development of Internet of Medical Things (IoMT) technology, many medical imaging equipments are connected to the medical information network to facilitate the process of diagnosing and treating for doctors. As medical image contains sensitive information, it is of importance yet very challenging to encrypt the medical image to safeguard the privacy or security of the patient. In this work, a deep learning based encryption and decryption network (DLEDNet) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DLEDNet, the Cycle-GAN network is employed as the main learning network to transfer the medical image from its original domain to the target domain. Target domain is regarded as a "Hidden Factors" to guide the learning model to realize the encryption process. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve an image decryption process. In order to realize the data mining process direct from the privacy-protected environment, a ROI (Region of interest)-mining-network is proposed to extract the interested object directly from the encrypted image. The proposed DLEDNet is evaluated on the chest X-ray dataset. Extensive experimental results show that the proposed method can achieve a high level of security with a good performance in efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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A lightweight isolation mechanism for secure branch predictors

arXiv

引用

arXiv 2020年

作者： Zhao, Lutan Li, Peinan Hou, Rui Huang, Michael C. Li, Jiazhen Zhang, Lixin Qian, Xuehai Meng, Dan State Key Laboratory of Information Security Institute of Information Engineering CAS and University of Chinese Academy of Sciences University of Rochester Institute of Computing Technology CAS University of Southern California

Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different programs, and such information from different processes are stored in the same structure and thus accessible to each other. This leaves the attackers with the opportunities for malicious training and malicious perception. Instead of flush-based or physical isolation of hardware resources, we want to achieve isolation of the content in these hardware tables with some lightweight processing using randomization as follows. (1) Content encoding. We propose to use hardware-based thread-private random numbers to encode the contents of the branch predictor tables (both direction and destination histories) which we call XOR-BP. Specifically, the data is encoded by XOR operation with the key before written in the table and decoded after read from the table. Such a mechanism obfuscates the information adding difficulties to cross-process or cross-privilege level analysis and perception. It achieves a similar effect of logical isolation but adds little in terms of space or time overheads. (2) Index encoding. We propose a randomized index mechanism of the branch predictor (Noisy-XOR-BP). Similar to the XOR-BP, another thread-private random number is used together with the branch instruction address as the input to compute the index of the branch predictor. This randomized indexing mechanism disrupts the correspondence between the branch instruction address and the branch predictor entry, thus increases the noise for malicious perception attacks. Our analyses using an FPGA-based RISC-V processor prototype and additional auxiliary simulations suggest that the proposed mechanisms incur a very small performance cost while providing strong *** Codes 68M01 (Primary) 68M99 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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Tile-based Multi-source Adaptive Streaming for 360-degree Ultra-High-Definition Videos

Tile-based Multi-source Adaptive Streaming for 360-degree Ul...

引用

International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Xinjing Yuan Lingjun Pu Ruilin Yun Jingdong Xu Institute of Systems and Networks College of Computer Science Nankai University Tianjin China State Key Laboratory of Integrated Services Networks (Xidian University) Xi’an China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

360° UHD videos have absorbed great attention in recent years. However, as they are of significant size and usually watched from a close range, they require extremely high bandwidth for a good immersive experience, which poses a great challenge on the current single-source adaptive streaming strategies. Realizing the great potentials of tile-based video streaming and pervasive edge services, we advocate a tile-based multi-source adaptive streaming strategy for 360° UHD videos over edge networks. In order to reap its benefits, we consider a comprehensive model which captures the key components of tile-based multi-source adaptive streaming. Then we formulate a joint bitrate selection and request scheduling problem, aiming at maximizing the system utility (i.e., user QoE minus service overhead) while satisfying the service integrity and latency constraints. To solve the formulated non-linear integer programming problem efficiently, we decouple the control variables and resort to matroid theory to design an optimal master-slave algorithm. In addition, we improve our proposed algorithm with a deep learning-based bitrate selection algorithm, which can achieve a rationalization result in a short running time. Extensive datadriven simulations validate the superior performance of our proposed algorithm.

关键词： Integer programming Deep learning Adaptation models Adaptive systems Bit rate Master-slave Videos

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