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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Yuhan Chen Xuehui Du Wei Xiao Haiyang Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing

Reconfigurable cipher chip has become a research hotspot because of its flexibility, security and good resource utilization. At present, research of reconfigurable cipher chip usually focuses on the s

ISBN: (纸本)9781467389808

关键词： Erbium

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Coherent H ∞ control for Markovian jump linear quantum systems

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IFAC-PapersOnLine 2020年第2期53卷 269-274页

作者： Yanan Liu Daoyi Dong Ian R. Petersen Qing Gao Steven X. Ding Hidehiro Yonezawa School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia Center for Quantum Computation and Communication Technology Australian Research Council Canberra ACT 2600 Australia Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen. 47057 Duisburg Germany Research School of Electrical Energy and Materials Engineering The Australian National University Canberra ACT 2601 Australia School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing and also with Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H ∞ control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H ∞ controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).

关键词： Coherent quantum feedback H∞ control fault-tolerant control Markovian jump linear quantum systems

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Image Classification for Malware Detection using Extremely Randomized Trees 11

Image Classification for Malware Detection using Extremely R...

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2017 11th IEEE International Conference on Anti-counterfeiting,Security,and Identification(ASID)

作者： Xin Zhou Jianmin Pang Guanghui Liang State Key Laboratory of Mathematical Engineering and Advanced Computing

ISBN: (纸本)9781538605349;9781538605332

With the development of machine learning and computer vision, we combined the image classification and malware detection in this work. Based on the technology of image classification, we achieved the detection of malware, using machine learning. In our work, we visualized the malware as a grayscale image and extracted texture features by Gabor filter. Depend on machine learning, we used extremely randomized trees as the classification and 10-fold cross validation to value it. Compared with GBDT, KNN and RF, experimental results are quite impressive with the accuracy rate of 96.19% and the recall rate of 97.51% on a malware database of 15,781 samples.

关键词： component malware image classification machine learning extremely randomized trees

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Improvement in Diversify Active Defense for Web Application by Using Language and Database Heterogeneity 11

Improvement in Diversify Active Defense for Web Application ...

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2017 11th IEEE International Conference on Anti-counterfeiting,Security,and Identification(ASID)

作者： Weichao Li Zheng Zhang Liqun Wang State Key Laboratory of Mathematical Engineering and Advanced Computing

ISBN: (纸本)9781538605349;9781538605332

According to OWASP selected web application Top 10 vulnerabilities in 2013 and 2017,structured query language(SQL)injection is consistently ranked the 1 ***,the protection of SQL injection,which is one of the most prevalent and pernicious security issues,requires ***,there are numerous unknown vulnerabilities and potential threats in *** this case,the active defense based on structural diversity can play an effective role to prevent the hacker from exploiting known or unknown *** on the basis of structural diversity,we propose a modified method,heterogeneous language,combined with heterogeneous database in data storage layer,to establish an active defense model for data *** empirically assess the impact of the vulnerability and conclude by testing the accuracy and performance,showing that our security model can not suffer from the same vulnerability as the unprotected ***,the future work and research direction are discussed.

关键词： Cyberspace security Active defence Heterogeneity Web application Database SQL labeling

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Bintaint: A Static Taint Analysis Method for Binary Vulnerability Mining

Bintaint: A Static Taint Analysis Method for Binary Vulnerab...

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International Conference on Cloud computing, Big Data and Blockchain (ICCBB)

作者： Zenan Feng Zhenyu Wang Weiyu Dong Rui Chang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728112787

Vulnerabilities in the current network space has been increasingly concerned by all parties. More and more software is currently in the form of binary code in practical applications. Therefore, the research on vulnerability mining technology for binary code attracts more attention from the researchers. In order to address the problem of binary vulnerability mining, this paper focuses on the taint analysis, and proposes a method called Bintaint, which can perform static taint analysis and generate the taint control flow graph (TCFG). In addition, we implement a system based on Bintaint to reduce the path explosion in the traditional vulnerability analysis process. Combined with our bidirectional search technology and path guidance algorithm to assist the symbol execution technology to generate test cases, the high computational overhead caused by complex constraints can be reduced. Finally, we evaluate our method on X86 programs and six core programs in different architecture embedded devices, and the result shows that our method detect all known vulnerability without false negative and mitigates the problem of path explosion and computational overhead effectively.

关键词： Decoding Data mining Explosions Tools Pollution Software Flow graphs

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Vulnerability Model and Evaluation of the UEFI Platform Firmware Based on Improved Attack Graphs

Vulnerability Model and Evaluation of the UEFI Platform Firm...

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Fei Cao Qingbao Li Zhifeng Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781538665664;9781538665657

Targeted at the situation of rampant attack on UEFI Platform Firmware, this paper summarizes the UEFI platform firmware framework structure as well as its potential security problems. Then the vulnerability factors of UEFI platform firmware are described using the modeling language. This paper proposes an improved Attack Graphs model based on Finite state Machine (AGFSM), which can be used to evaluate the reliability of UEFI, as well as calculate the expected exploitation cost and expected attack loss. Finally, based on the AGFSM model, we realize the evaluation of a set of attack strategies for the UEFI platform firmware and verify the rationality of the model and the validity of the evaluation. The research is conducive to the rapid detection of the vulnerability of UEFI Platform Firmware, predict the attack paths and deploy the security strategy targeted to safeguard the firmware.

关键词： Microprogramming Security Hardware Protocols Reliability Standards Read only memory

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Detection of data injection attack in industrial control system using long short term memory recurrent neural network

Detection of data injection attack in industrial control sys...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Wei Wang Yaobin Xie Lu Ren Xiaodong Zhu Rui Chang Qing Yin State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

In 2010, the outbreak of Stuxnet sounded a warning in the field of industrial ***. As the major attack form of Stuxnet, data injection attack is characterized by high concealment and great destructiveness. This paper proposes a new method to detect data injection attack in Industrial Control Systems (ICS), in which Long Short Term Memory Recurrent Neural Network (LSTM-RNN) is a temporal sequences predictor. We then use the Euclidean detector to identify attacks in a model of a chemical plant. With simulation and evaluation in Tennessee Eastman (TE) process, we show that this method is able to detect various types of data injection attacks.

关键词： Detectors Inductors Integrated circuits Temperature sensors Recurrent neural networks Security Logic gates

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HPC AI500: A Benchmark Suite for HPC AI Systems 1st

HPC AI500: A Benchmark Suite for HPC AI Systems

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Jiang, Zihan Gao, Wanling Wang, Lei Xiong, Xingwang Zhang, Yuchen Wen, Xu Luo, Chunjie Ye, Hainan Lu, Xiaoyi Zhang, Yunquan Feng, Shengzhong Li, Kenli Xu, Weijia Zhan, Jianfeng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Dover DE United States Beijing Academy of Frontier Sciences and Technology Beijing China State University of New York Buffalo United States Department of Computer Science and Engineering The Ohio State University Columbus United States National Supercomputing Center in Shenzhen Shenzhen China National Supercomputing Center in Changsha Changsha China National Supercomputing Center in Jinan Jinan China Texas Advanced Computing Center The Texas University at Austin Austin United States

ISBN: (纸本)9783030328122

In recent years, with the trend of applying deep learning (DL) in high performance scientific computing, the unique characteristics of emerging DL workloads in HPC raise great challenges in designing, implementing HPC AI systems. The community needs a new yard stick for evaluating the future HPC systems. In this paper, we propose HPC AI500—a benchmark suite for evaluating HPC systems that running scientific DL workloads. Covering the most representative scientific fields, each workload from HPC AI500 is based on real-world scientific DL applications. Currently, we choose 14 scientific DL benchmarks from perspectives of application scenarios, data sets, and software stack. We propose a set of metrics for comprehensively evaluating the HPC AI systems, considering both accuracy, performance as well as power and cost. We provide a scalable reference implementation of HPC AI500. The specification and source code are publicly available from http://***/HPCAI500/***. Meanwhile, the AI benchmark suites for datacenter, IoT, Edge are also released on the BenchCouncil web site. © 2019, Springer Nature Switzerland AG.

关键词： Benchmarking

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KE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing

KE-GAN: Knowledge Embedded Generative Adversarial Networks f...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Mengshi Qi Yunhong Wang Jie Qin Annan Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Inception Institute of Artificial Intelligence UAE

In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecting the rich semantic knowledge and inter-object relationships in the scene. In addition, these methods usually require a large number of pixel-level annotations, which is too expensive in practice. In this paper, we propose a novel Knowledge Embedded Generative Adversarial Networks, dubbed as KE-GAN, to tackle the challenging problem in a semi-supervised fashion. KE-GAN captures semantic consistencies of different categories by devising a Knowledge Graph from the large-scale text corpus. In addition to readily-available unlabeled data, we generate synthetic images to unveil rich structural information underlying the images. Moreover, a pyramid architecture is incorporated into the discriminator to acquire multi-scale contextual information for better parsing results. Extensive experimental results on four standard benchmarks demonstrate that KE-GAN is capable of improving semantic consistencies and learning better representations for scene parsing, resulting in the state-of-the-art performance.

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A tera-electronvolt afterglow from a narrow jet in an extremely bright gamma-ray burst 221009A

arXiv

引用

arXiv 2023年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, H.L. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Long, W.J. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Nan, Y.C. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, C. Wang, C.B. Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Tianfu Cosmic Ray Research Center Sichuan Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institute for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Zhuhai 519000 China Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Department of Engineering Physics Tsinghua University Beijing1

Some gamma-ray bursts (GRBs) have an afterglow in the tera-electronvolt (TeV) band, but the early onset of this afterglow has not been observed. We report observations with the Large High Altitude Air Shower Observatory of the bright GRB 221009A, which serendipitously occurred within the instrument field of view. More than 64,000 photons (above 0.2 TeV) were detected within the first 3000 seconds. The TeV photon flux began several minutes after the GRB trigger, then rose to a flux peak about 10 seconds later. This was followed by a decay phase, which became more rapid at ∼ 650 s after the peak. The emission can be explained with a relativistic jet model with half-opening angle ∼ 0.8◦, consistent with the core of a structured jet. This interpretation could explain the high isotropic energy of this GRB. Copyright © 2023, The Authors. All rights reserved.

关键词： Photons

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