检索结果-内蒙古大学图书馆

Enhancing Security of FPGA-Based Embedded Systems with Combinational Logic Binding

学校读者我要写书评

暂无评论

Journal of computer Science & Technology 2017年第2期32卷 329-339页

作者： Ji-Liang Zhang Wei-Zheng Wang Xing-Wei Wang Zhi-Hua Xia Software College Northeastern University Shenyang 110169 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China Department of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410014 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

With the increasing use of field-programmable gate arrays （FPGAs） in embedded systems and many embedded applications, the failure to protect FPGA-based embedded systems from cloning attacks has brought serious losses to system developers. This paper proposes a novel combinational logic binding technique to specially protect FPGA-based embedded systems from cloning attacks and provides a pay-per-device licensing model for the FPGA market. Security analysis shows that the proposed binding scheme is robust against various types of malicious attacks. Experimental evaluations demonstrate the low overhead of the proposed technique.

关键词： cloning attack reverse engineering FPGA （field-programmable gate array） security hardware security

Overview of the CCKS 2019 knowledge graph evaluation track: Entity, relation, event and QA

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Han, Xianpei Wang, Zhichun Zhang, Jiangtao Wen, Qinghua Li, Wenqi Tang, Buzhou Wang, Qi Feng, Zhifan Zhang, Yang Lu, Yajuan Wang, Haitao Chen, Wenliang Shao, Hao Chen, Yubo Liu, Kang Zhao, Jun Wang, Taifeng Zhang, Kezun Wang, Meng Jiang, Yinlin Qi, Guilin Zou, Lei Hu, Sen Zhang, Minhao Lin, Yinnian Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence Beijing Normal University Beijing100875 China 305 Hospital of People’s Liberation Army Beijing100017 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Yidu Cloud Beijing100000 China Harbin Institute of Technology Shenzhen Guangdong518055 China Baidu Beijing100193 China School of Computer Science & Technology Soochow University Suzhou Jiangsu215006 China Gowild Suzhou Jiangsu215002 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Ant Financial Hangzhou Zhejiang310000 China School of Computer Science and Engineering Southeast University Nanjing Jiangsu211189 China Key Laboratory of Computer Network Information Integration Southeast University Nanjing Jiangsu211189 China Peking University Beijing100871 China

Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation track with 6 tasks and attracted more than 1,600 teams. In this paper we give an overview of the knowledge graph evaluation tract at CCKS 2019. By reviewing the task definition, successful methods, useful resources, good strategies and research challenges associated with each task in CCKS 2019, this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

Adaptive sampling for openflow network measurement methods 17

学校读者我要写书评

暂无评论

Adaptive sampling for openflow network measurement methods

12th International Conference on Future Internet Technologies, CFI 2017

作者： Cheng, Guang Yu, Jun School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211198 China

ISBN: (纸本)9781450353328

OpenFlow provides a statistics collection scheme for per-flow and aggregate metrics at a user-specified frequency. However, periodic polling for flow statistics cannot well balance the tradeofi between measurement accuracy and limited control channel bandwidth. To further discuss the resource/accuracy tradeofi, this paper extends OpenFlow protocol to add sampling action for each monitoring flow entry, and systematically sample the matching packets to infer the flow-level statistics. Although traffic sampling can somehow be error-prone, it will provide near-real-Time measurements of flow dynamics and determine its accurate polling frequency, which the polling-based approach cannot achieve. In this paper, we propose a per-flow sampling solution to instruct the controller to adaptively adjust polling frequency, then evaluate it in the context of link utilization monitoring. © 2017 Association for Computing Machinery.

关键词： Software defined networking

Fractional spectral graph wavelets and their applications

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Wu, Jiasong Wu, Fuzhi Yang, Qihan Kong, Youyong Liu, Xilin Zhang, Yan Senhadji, Lotfi Shu, Huazhong LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China International Joint Research Laboratory of Information Display and Visualization Southeast University Ministry of Education Nanjing210096 China INSERM U 1099 Université de Rennes 1 LTSI Rennes35000 France Centre de Recherche en Information Biomédicale Sino-francais Nanjing210096 China College of Data Science Taiyuan University of Technology Taiyuan030024 China

One of the key challenges in the area of signal processing on graphs is to design transforms and dictionaries methods to identify and exploit structure in signals on weighted graphs. In this paper, we first generalize graph Fourier transform (GFT) to graph fractional Fourier transform (GFRFT), which is then used to define a novel transform named spectral graph fractional wavelet transform (SGFRWT), which is a generalized and extended version of spectral graph wavelet transform (SGWT). A fast algorithm for SGFRWT is also derived and implemented based on Fourier series approximation. The potential applications of SGFRWT are also presented. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal processing

Umbrella: Enabling ISPs to offer readily deployable and privacy-preserving DDoS prevention services

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Liu, Zhuotao Cao, Yuan Zhu, Min Ge, Wei Electrical and Computer Engineering Department University of Illinois at Urbana-Champaign UrbanaIL61801 United States Google Inc. Mountain ViewCA94043 United States College of Internet of Things Engineering Hohai University Changzhou213022 China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Research Institute of Applied Technologies Tsinghua University China Electrical Engineering Department Southeast University China

Defending against distributed denial of service (DDoS) attacks in the Internet is a fundamental problem. However, recent industrial interviews with over 100 security experts from more than ten industry segments indicate that DDoS problems have not been fully addressed. The reasons are twofold. On one hand, many academic proposals that are provably secure witness little real-world deployment. On the other hand, the operation model for existing DDoS-prevention service providers (e.g., Cloudflare, Akamai) is privacy invasive for large organizations (e.g., government). In this paper, we present Umbrella, a new DDoS defense mechanism enabling Internet Service Providers (ISPs) to offer readily deployable and privacy-preserving DDoS prevention services to their customers. At its core, Umbrella develops a multi-layered defense architecture to defend against a wide spectrum of DDoS attacks. In particular, the flood throttling layer stops amplification-based DDoS attacks;the congestion resolving layer, aiming to prevent sophisticated attacks that cannot be easily filtered, enforces congestion accountability to ensure that legitimate flows are guaranteed to receive their fair shares regardless of attackers' strategies;and finally the user-specific layer allows DDoS victims to enforce self-desired traffic control policies that best satisfy their business requirements. Based on Linux implementation, we demonstrate that Umbrella is capable to deal with large scale attacks involving millions of attack flows, meanwhile imposing negligible packet processing overhead. Further, our physical testbed experiments and large scale simulations prove that Umbrella is effective to mitigate various DDoS attacks. Copyright © 2019, The Authors. All rights reserved.

关键词： Denial-of-service attack

Robust unsupervised small area change detection from SAR imagery using deep learning

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Zhang, Xinzheng Su, Hang Zhang, Ce Gu, Xiaowei Tan, Xiaoheng Atkinson, Peter M. School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion Chongqing400044 China Lancaster Environment Centre Lancaster University LancasterLA1 4YQ United Kingdom UK Centre for Ecology & Hydrology Library Avenue LancasterLA1 4AP United Kingdom Department of Computer Science Aberystwyth University AberystwythSY23 3DB United Kingdom Geography and Environmental Science University of Southampton Highfield SouthamptonSO17 1BJ United Kingdom Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences 11A Datun Road Beijing100101 China

Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multitemporal SAR images using deep learning. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection. Copyright © 2020, The Authors. All rights reserved.

关键词： Synthetic aperture radar

Supervised discriminative sparse PCA for com-characteristic gene selection and tumor classification on multiview biological data

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Feng, Chun-Mei Xu, Yong Liu, Jin-Xing Gao, Ying-Lian Zheng, Chun-Hou Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Key Laboratory of Network Oriented Intelligent Computation Shenzhen518055 China School of Information Science and Engineering Qufu Normal University Rizhao276826 China Library of Qufu Normal University Qufu Normal University Rizhao276826 China School of Computer Science and Technology Anhui University Hefei230000 China

Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods;moreover, the class ambiguity within the sample is high. To overcome this problem, this paper developed a new PCA method, which is named the supervised discriminative sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via the linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over the traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed, and its convergence proof is provided. SDSPCA has been applied to the common-characteristic gene selection and tumor classification on multiview biological data. The sparsity and classification performance of SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Principal component analysis

Link prediction in complex networks via modularity-based belief propagation

学校读者我要写书评

暂无评论

Chinese Physics B 2017年第3期26卷 604-614页

作者：赖大荣舒欣 Christine Nardini School of Computer Science and Engineering Southeast University Nanjing 211189 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing 210096 China College of Information Science and Technology Nanjing Agricultural University Nanjing 210095 China CNR-IAC 'Mauro Picone' Via dei Taurini 19 00185 Roma Italy Personalgenomics Via delle Grazie Verona Italy

Link prediction aims at detecting missing, spurious or evolving links in a network, based on the topological information and/or nodes＇ attributes of the network. Under the assumption that the likelihood of the existence of a link between two nodes can be captured by nodes＇ similarity, several methods have been proposed to compute similarity directly or indirectly, with information on node degree. However, correctly predicting links is also crucial in revealing the link formation mechanisms and thus in providing more accurate modeling for networks. We here propose a novel method to predict links by incorporating stochastic-block-model link generating mechanisms with node degree. The proposed method first recov- ers the underlying block structure of a network by modularity-based belief propagation, and based on the recovered block structural information it models the link likelihood between two nodes to match the degree sequence of the network. Experiments on a set of real-world networks and synthetic networks generated by stochastic block model show that our proposed method is effective in detecting missing, spurious or evolving links of networks that can be well modeled by a stochastic block model. This approach efficiently complements the toolbox for complex network analysis, offering a novel tool to model links in stochastic block model networks that are fundamental in the modeling of real world complex networks.

关键词： link prediction complex network belief propagation modularity

Spatial search for a general multi-vertex state on graph by continuous-time quantum walks

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Li, Xi Chen, Hanwu Ruan, Yue Liu, Zhihao Xu, Mengke Tan, Jianing School of Computer Science and Engineering Southeast University Nanjing210096 China School of Computer Science and Technology Anhui University of Technology Maanshan243005 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China

In this work, we consider the spatial search for a general marked state on graphs by continuous time quantum walks. As a simplest case, we compute the amplitude expression of the search for the multi-vertex uniform superposition state on hypercube, and find that the spatial search algorithm is optimal for the two-vertex uniform state. However, on general graphs, a common formula can't be obtained for searching a general non-uniform superposition state. Fortunately, a Laplacian spectrum condition which determines whether the associated graph could be appropriate for performing the optimal spatial search is presented. The condition implies that if the proportion of the maximum and the non-zero minimum Laplacian eigenvalues is less or equal to 1+1/√2, then the spatial search is optimal for any general state. At last, we apply this condition to three kind graphs, the induced complete graph, the strongly regular graph and the regular complete multi-partite graphs. By the condition, one can conclude that these graphs will become suitable for optimal search with properly setting their graph parameters. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphic methods