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arXiv 2019年

作者： Zhou, Yuyang Cheng, Guang Jiang, Shanqing Dai, Mian School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Jiangsu Provincial Key Laboratory of Computer Network Technology Southeast University Nanjing China National Key Laboratory of Science and Technology on Information System Security Beijing China

Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Data mining

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Research on security Protocol Analysis Tool SmartVerif

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Journal of Physics: Conference Series 2021年第1期2132卷

作者： Siqi Lu Qingdi Han Xuyang Miao Yubo Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450000 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan 450000 China Department of Information Science and Engineering Ocean University of China Qingdao Shandong 266100 China

security protocols have been designed to protect the security of the network. However, many security protocols cannot guarantee absolute security in real applications. Therefore, security tests of the network protocol become particularly important. In this paper, firstly, we introduce SmartVerif, which is the first formal analysis tool to automatically verify the security of protocols through dynamic strategies. And then, we use SmartVerif to verify the pseudo-randomness of the encapsulated key of the Two-Pass AKE protocol, which was proposed by Liu's in ASIACRYPT in 2020. Finally, we summary our work and show some limitations of SmartVerif. At the same time, we also point out the direction for future improvement of SmartVerif.

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Batch face alignment using a low-rank Gan

arXiv

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arXiv 2019年

作者： Huang, Jiabo Xie, Xiaohua Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

This paper studies the problem of aligning a set of face images of the same individual into a normalized image while removing the outliers like partial occlusion, extreme facial expression as well as significant illumination variation. Our model seeks an optimal image domain transformation such that the matrix of misaligned images can be decomposed as the sum of a sparse matrix of noise and a rank-one matrix of aligned images. The image transformation is learned in an unsupervised manner, which means that ground-truth aligned images are unnecessary for our model. Specifically, we make use of the remarkable non-linear transforming ability of generative adversarial network(GAN) and guide it with low-rank generation as well as sparse noise constraint to achieve the face alignment. We verify the efficacy of the proposed model with extensive experiments on real-world face databases, demonstrating higher accuracy and efficiency than existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Depthwise Separable Convolution Feature Learning for Ihomogeneous Rock Image Classification 1

引用

4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018

作者： Zhu, Yueqin Bai, Lin Peng, Weihang Zhang, Xinyang Luo, Xiong Development and Research Center China Geological Survey Beijing100037 China Key Laboratory of Geological Information Technology Ministry of Natural Resources Beijing100037 China Geomathematics Key Laboratory of Sichuan Province Chengdu University of Technology Chengdu610059 China College of Geophysics Chengdu University of Technology Chengdu610059 China College of Management Science Chengdu University of Technology Chengdu610059 China College of Network Security Chengdu University of Technology Chengdu610059 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9789811379833

ISBN: (纸本)9789811379826

The issue of classifying rock images is one of most important problems in geological detection and analysis. In order to improve rock recognition performance, we use convolution neural network (CNN) based deep learning method to classify ihomogeneous rock images intelligently. We present the depthwise separable convolution method for rock image classification, which reduces the required parameters compared to normal convolution, and achieves the separation of channels and regions. In our experiments, there are 12 kinds of common rock image data collected by us. Generally, similar rocks such as limestone and dolomite are easy to be confused, which is the same as that of rock with naked eyes. Compared with the model Inception, the rock image classification accuracy can be improved 9% by using the depth separable convolution model Xception. Meanwhile, after analyzing feature maps of granite and slate generated by the proposed model, we can easily find that the color, mineral composition and structure of the rock image are extracted successfully. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Allocating limited resources to protect a massive number of targets using a game theoretic model

arXiv

引用

arXiv 2019年

作者： Liu, Xu Di, Xiaoqiang Li, Jinqing Wang, Huan Zhao, Jianping Yang, Huamin Cong, Ligang Jiang, Yuming School of Computer Science and Technology Changchun University of Science and Technology Changchun China Jilin Province Key Laboratory of Network and Information Security Changchun China Department of Information Security and Communication Technology Norwegian University of Science and Technology Trondheim Norway

Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a tradeoff between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness. Copyright © 2019, The Authors. All rights reserved.

关键词： Resource allocation

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Spatiotemporal characteristics and factor analysis of SARS-CoV-2 infections among healthcare workers in Wuhan, China

arXiv

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arXiv 2020年

作者： Wang, Peixiao Ren, Hui Zhu, Xinyan Fu, Xiaokang Liu, Hongqiang Hu, Tao State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Center for Geographic Analysis Harvard University CambridgeMA02138 United States College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao266590 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan University Wuhan430079 China

Studying the spatiotemporal distribution of SARS-CoV-2 infections among healthcare workers (HCWs) can aid in protecting them from exposure. In this study, an open-source dataset of HCW diagnoses was provided, and the spatiotemporal distributions of SARS-CoV-2 infections among HCWs in Wuhan, China were explored. A geographical detector technique was then used to investigate the impacts of hospital level, type, distance from the infection source, and other external indicators of HCW infections. The results showed that the number of daily HCW infections over time in Wuhan followed a log-normal distribution, with its mean observed on 23rd January 2020 and a standard deviation of 10.8 days. The implementation of high-impact measures, such as the lockdown of the city, may have increased the probability of HCW infections in the short term, especially for those in the outer ring of Wuhan. The infection of HCWs Wuhan exhibited clear spatial heterogeneity. The number of HCW infections was higher in the central city and lower in the outer city. Moreover, HCW infections displayed significant spatial autocorrelation and dependence. Factor analysis revealed that hospital level and type had an even greater impact on HCW infections;third-class and general hospitals closer to infection sources were correlated with especially high risks of infection. © 2020, CC BY.

关键词： SARS

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Provably Secure APK Redevelopment Authorization Scheme in the Standard Model

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Computers, Materials & Continua 2018年第9期56卷 447-465页

作者： Daofeng Li Mingxing Luo Bowen Zhao Xiangdong Che School of Computer Electrical and informationGuang Xi UniversityNanning530004China Information Security and National Computing Grid Laboratory Southwest Jiaotong UniversityChengdu610031China Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing Guangxi UniversityNanning530004China School of Information Security&Applied Computing College of TechnologyEastern Michigan UniversityMichigan48197USA

The secure issues of APK are very important in Android *** order to solve potential secure problems and copyrights issues in redevelopment of APK files,in this paper we propose a new APK redevelopment mechanism(APK-SAN).By exploring sanitizable signature technology,APK-SAN allows the original developer to authorize specified modifier who can redevelop the designated source code of APK *** scheme does not require interactions between the developer and *** can reduce the communication overhead and computational overhead for ***,the signature of redeveloped APK files is valid and maintains the *** proposed APK-SAN signature can effectively protect the security of the redeveloped APK files and copyrights of the developer and modifier.

关键词： Sanitizable signature APK signature mechanism redevelopment ID-based signature scheme

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Faking photon number on a transition-edge sensor

arXiv

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arXiv 2021年

作者： Chaiwongkhot, Poompong Zhong, Jiaqiang Huang, Anqi Qin, Hao Shi, Sheng-Cai Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Bangkok10110 Thailand Purple Mountain Observatory and Key Laboratory of Radio Astronomy Chinese Academy of Sciences 10 Yuanhua road Nanjing210033 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China CAS Quantum Network Co. Ltd. 99 Xiupu road Shanghai201315 China Russian Quantum Center Skolkovo Moscow121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

We study potential security vulnerabilities of a single-photon detector based on superconducting transition-edge sensor. In a simple experiment, we show that an adversary could fake a photon number result at a certain wavelength by sending a larger number of photons at a longer wavelength. In another experiment, we show that the detector can be blinded by bright continuous-wave light and then, a controlled response simulating single-photon detection can be produced by applying a bright light pulse. We model an intercept- and-resend attack on a quantum key distribution system that exploits the latter vulnerability and, under certain assumptions, succeeds to steal the key. © 2021, CC BY.

关键词： Photons

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Front Cover: International Journal of Intelligent Systems, Volume 37 Issue 11 November 2022

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International Journal of Intelligent Systems 2022年第11期37卷

作者： Xianjia Meng Yong Yang Ximeng Liu Nan Jiang School of Information Science and Technology Northwest University Xi'an People's Republic of China Fujian Province Key Laboratory of Information Security of Network System and College of Computer and Data Science Fuzhou University Fuzhou People's Republic of China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen People's Republic of China Department of Internet of Things School of Information Engineering East China Jiao Tong University Nanchang People's Republic of China

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Using Reduced Graphene Oxide to Generate Q-Switched Pulses in Er-Doped Fiber Laser

引用

Chinese Physics Letters 2018年第11期35卷 32-34页

作者： Lu Li Rui-Dong Lv Si-Cong Liu Zhen-Dong Chen Jiang Wang Yong-Gang Wang Wei Ren School of Science Xi＇an University of Posts and TelecommunicationsXi＇an 710121 School of Physics and information Technology Shaanxi Normal UniversityXi＇an 710119 Shaanxi Key Laboratory of Information Communication Network and Security Xi＇an University of Posts and TelecommunicationsXi＇an 710121

Using the reduced graphene oxide（rGO） as a saturable absorber（SA） in an Er-doped fiber（EDF） laser cavity,we obtain the Q-switching operation. The rGO SA is prepared by depositing the GO on fluorine mica（FM） using the thermal reduction method. The modulation depth of rGO/FM is measured to be 3.2%. By incorporating the rGO/FM film into the EDF laser cavity, we obtain stable Q-switched pulses. The shortest pulse duration is3.53 μs, and the maximum single pulse energy is 48.19 nJ. The long-term stability of working is well *** experimental results show that the rGO possesses potential photonics applications.

关键词： EDF GO Using Reduced Graphene Oxide to Generate Q-Switched Pulses in Er-Doped Fiber Laser SA

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