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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

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Steganalysis with CNN Using Multi-channels Filtered Residuals 3rd

Steganalysis with CNN Using Multi-channels Filtered Residual...

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3rd international conference on Cloud computing and Security (ICCCS)

作者： Yuan, Yafei Lu, Wei Feng, Bingwen Weng, Jian Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Guangdong Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China

ISBN: (纸本)9783319685052;9783319685045

In the current study of steganalysis, Convolutional Neural Network (CNN) have attracted many scholars' attention. Recently, some effective CNN architectures have been proposed with better results than traditional Rich Models with Ensemble Classifiers. Inspired by the idea that Rich Models use various types of sub-models to enlarge different characteristics between cover and stego features, a scheme based on multi-channels filtered residuals is proposed for digital image steganalysis in this paper. This paper mainly focus on the stage of image processing, 3 high-pass filtered image residuals are fed to a deep CNN architecture to make full use of the great nonlinear curve fitting capability. As known, deep learning is powerful in pattern recognition, most previous networks only use single type of filtered residuals in steganalysis, varied high-pass filtered residuals can offer stronger features for CNN in this paper. After filtering, the residuals are superposed into a multi-channels residual map before training, this measure can involve a joint optimization of CNN's parameters. But single residual map has no such effect. The experiment results prove that it's an efficient way to provide a better detection performance, achieving an accuracy of 82.02% on Cropped-BOSSBase-1.01 dataset.

关键词： Steganalysis CNN Multi-channels Residual

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Spatiotemporal Radio Tomographic Imaging with Bayesian Compressive Sensing for RSS-Based Indoor Target Localization 3rd

Spatiotemporal Radio Tomographic Imaging with Bayesian Compr...

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3rd international conference on Cloud computing and Security (ICCCS)

作者： Shang, Baolin Tan, Jiaju Hong, Xiaobing Guo, Xuemei Wang, Guoli Liu, Gonggui Xue, Shouren Sun Yat Sen Univ Sch Data & Comp Sci Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou Guangdong Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Inst Robot & Automat Informat Syst Tianjin Peoples R China Vkan Certificat & Testing Co Ltd Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783319685427;9783319685410

Wireless sensor network based device-free localization (DFL) is now widely used in security and monitoring systems for indoor and outdoor areas. Multipath fading induced noises often degrade the performance of the DFL security system. To address this problem, the paper firstly presents a spatiotemporal radio tomographic imaging (RTI) approach for the enhancement of localization. Specifically, the task of RTI can be formulated into a sparse Bayesian learning problem. In addition, two robust sparse Byesian learning algorithms are developed to handle with the low signal-to-noise-ratio (SNR) with heterogeneous noise. The proposed spatiotemporal RTI approach performs much better than traditional RTI with lower average errors in our four diverse cluttered indoor scenes. The localization results also highlight advantages of applying proposed robust sparse Bayesian learning algorithms in addressing missing estimations and outlier errors, and finally improving indoor target DFL performance.

关键词： Radio tomographic imaging Target localization Sparse bayesian learning Wireless sensor network

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Scalable automated analysis of access control and privacy policies 3rd

Scalable automated analysis of access control and privacy po...

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3rd international conference on Future data and Security Engineering, FDSE 2016 and the 10th international conference on Advanced computing and Applications, ACOMP 2016

作者： Truong, Anh Ranise, Silvio Nguyen, Thanh Tung Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam Security and Trust Unit FBK-Irst Trento Italy

ISBN: (纸本)9783662562659

Access Control is becoming increasingly important for today ubiquitous systems. Sophisticated security requirements need to be ensured by authorization policies for increasingly complex and large applications. As a consequence, designers need to understand such policies and ensure that they meet the desired security constraints while administrators must also maintain them so as to comply with the evolving needs of systems and applications. These tasks are greatly complicated by the expressiveness and the dimensions of the authorization policies. It is thus necessary to provide policy designers and administrators with automated analysis techniques that are capable to foresee if, and under what conditions, security properties may be violated. In this paper, we consider this analysis problem in the context of the Role-Based Access Control (RBAC), one of the most widespread access control models. We describe how we design heuristics to enable an analysis tool, called asaspXL, to scale up to handle large and complex Administrative RBAC policies. We also discuss the capability of applying the techniques inside the tool to the analysis of location-based privacy policies. An extensive experimentation shows that the proposed heuristics play a key role in the success of the analysis tool over the state-of-the-art analysis tools. © Springer-Verlag GmbH Germany 2017.

关键词： Access control models

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Improved STDM Watermarking Using Semantic Information-Based JND Model 3rd

Improved STDM Watermarking Using Semantic Information-Based ...

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3rd international conference on Cloud computing and Security (ICCCS)

作者： Wang, Chunxing Zhang, Teng Wan, Wenbo Sun, Jiande Li, Jing Xu, Meiling Shandong Normal Univ Sch Phys & Elect Jinan 250014 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Shandong Normal Univ Inst Data Sci & Technol Jinan 250014 Shandong Peoples R China Shandong Management Univ Sch Mech & Elect Engn Jinan 250100 Shandong Peoples R China

ISBN: (纸本)9783319685052;9783319685045

The perceptual just noticeable distortion (JND) model has attracted increasing attention in the field of the quantization-based watermarking framework. The JND model can provide a superior trade-off between robustness and fidelity. However, the conventional JND models are not fit for the quantization-based watermarking, as the image has been altered by watermarking embedding. In this paper, we present an improved spread transform dither modulation (STDM) watermarking scheme, which is based on the image primitive features produced according to JND mechanism. The procedures include the contrast masking effect by utilizing a new measurement of edge strength which represent semantic information. What's more, the proposed semantic information-based JND model can be theoretically invariant to the changes in the watermark-embedding processing. The newly proposed JND model is very simple but more effective in the STDM watermarking. Experiments results demonstrate that the proposed watermarking scheme can bring about better performance compared with previously proposed perceptual STDM schemes.

关键词： Watermarking JND Model STDM Semantic information

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An Android App Recommendation Approach by Merging Network Traffic Cost 3rd

An Android App Recommendation Approach by Merging Network Tr...

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3rd international conference on Cloud computing and Security (ICCCS)

作者： Su, Xin Liu, Xuchong Lin, Jiuchuan Tong, Yu Hunan Police Acad Hunan Prov Key Lab Network Invest Technol Changsha Hunan Peoples R China Hunan Police Acad Key Lab Network Crime Invest Hunan Prov Coll Changsha Hunan Peoples R China Hunan Police Acad Dept Informat Technol Changsha Hunan Peoples R China Minist Publ Secur Res Inst 3 Key Lab Informat Network Secur Shanghai Peoples R China

ISBN: (纸本)9783319685052;9783319685045

A large amount and different types of mobile applications are being offered to end users via app markets. Existing mobile app markets generally recommend the most popular mobile apps to mobile users for purpose of facilitate the proper selection of mobile apps. However, these apps normally generate network traffic, which will consumes user mobile data plan and may even cause potential security issues. Therefore, more and more mobile users are hesitant or even reluctant to use the mobile apps that are recommended by the mobile app markets. To fill this crucial gap, we propose a mobile app recommendation approach which can provide app recommendations by considering both the app popularity and their traffic cost. To achieve this goal, we first estimate app network traffic score based on bipartite graph. Then, we propose a flexible approach based on Benefit-Cost analysis, which can recommend apps by maintaining a balance between the app popularity and the traffic cost concern. Finally, we evaluate our approach with extensive experiments on a large-scale data set collected from Google Play. The experimental results clearly validate the effectiveness and efficiency of our approach.

关键词： Cost benefit analysis

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One-class collective anomaly detection based on LSTM-RNNs 3rd

One-class collective anomaly detection based on LSTM-RNNs

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3rd international conference on Future data and Security Engineering, FDSE 2016 and the 10th international conference on Advanced computing and Applications, ACOMP 2016

作者： Nguyen Thi, Nga Cao, Van Loi Le-Khac, Nhien-An Institute of Electronic Institute of Military Science and Technology Hanoi Viet Nam University College Dublin Dublin Ireland

ISBN: (纸本)9783662562659

Intrusion detection for computer network systems has been becoming one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to the valuable resources hosted on computer networks. Traditional misuse detection strategies are unable to detect new and unknown intrusion types. In contrast, anomaly detection in network security aims to distinguish between illegal or malicious events and normal behavior of network systems. Anomaly detection can be considered as a classification problem where it builds models of normal network behavior, of which it uses to detect new patterns that significantly deviate from the model. Most of the current approaches on anomaly detection is based on the learning of normal behavior and anomalous actions. They do not include memory that is they do not take into account previous events classify new ones. In this paper, we propose a one-class collective anomaly detection model based on neural network learning. Normally a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) is trained only on normal data, and it is capable of predicting several time-steps ahead of an input. In our approach, a LSTM RNN is trained on normal time series data before performing a prediction for each time-step. Instead of considering each time-step separately, the observation of prediction errors from a certain number of time-steps is now proposed as a new idea for detecting collective anomalies. The prediction errors of a certain number of the latest time-steps above a threshold will indicate a collective anomaly. The model is evaluated on a time series version of the KDD 1999 dataset. The experiments demonstrate that the proposed model is capable to detect collective anomaly efficiently. © Springer-Verlag GmbH Germany 2017.

关键词： Long short-term memory

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Plenary Panel: Convergence of High-Performance computing and Communication, Smart City, and data sciences and Systems: Fields Helping Grand Challenges and Each Other

Plenary Panel: Convergence of High-Performance Computing and...

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IEEE international conference on High Performance computing and Communications (HPCC)

Summary form only given. The complete presentation was not made available for publication as part of the conference proceedings. The plenary panel “Convergence of High-Performance computing and Communication, Smart City, and data sciences and Systems: Fields Helping Grand Challenges and Each Other” involved researchers from all three collocated conferences. Panel Moderator and Co-Organizer: H.J. Siegel, Professor Emeritus, previously Abell Endowed Chair Distinguished Professor of Electrical and Computer Engineering, and Professor of Computer science, Colorado State University; Fellow IEEE, Fellow ACM, HPCC Program Committee Co- Chair. Co-Organizer: Prof. Siddharth Suryanarayanan, Associate Professor and the Rhoden Professor in Electrical and Computer Engineering Dept., Colorado State University; Smart City Program Committee Member. This was a 90 minute panel. Panelists were requested to answer the list of four questions that follow. The moderator encouraged the panelists to critique the other panelists’ ideas so this was an interactive panel, not just a set of mini-talks. The interaction among panelists was followed by interaction between the panelists and the audience.

关键词： Smart cities Convergence Informatics

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Workload-Aware Page-Level Flash Translation Layer for NAND Flash-Based Storage Systems 3rd

Workload-Aware Page-Level Flash Translation Layer for NAND F...

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3rd international conference on Cloud computing and Security (ICCCS)

作者： Wang, Huibing Lin, Mingwei Xiong, Jinbo Lin, Li Xiao, Ruliang Fujian Normal Univ Coll Math & Informat Fuzhou 350108 Fujian Peoples R China Fujian Engn Res Ctr Publ Serv Big Data Min & Appl Fuzhou 350108 Fujian Peoples R China

ISBN: (纸本)9783319685427;9783319685410

Demand-based flash translation layer is an efficient page-level flash translation layer, which can effectively reduce the RAM (Random Access Memory) footprint of NAND flash-based storage systems. However, this demand-based flash translation layer does not consider the spatial locality of workloads. In this paper, a new workload-aware page-level flash translation layer is proposed for NAND flash-based storage systems. The proposed flash translation layer maintains three caches in RAM to cache mapping entries, which are the on-demand mapping entry cache, frequent mapping entry cache, and dirty mapping entry cache. Considering both temporal locality and spatial locality of workloads, the on-demand mapping entry cache is designed to store the on-demand mapping entries and sequential mapping entries. Considering the access frequency of workloads, the frequent mapping entry cache is designed to cache the most frequently accessed mapping entries. To decrease the number of updates to translation pages, the dirty mapping entry cache is designed to cache the dirty mapping entries and flush the dirty mapping entries belonging to the same translation page to NAND flash memory in a batch mode. The experimental results show that the proposed flash translation layer performs better than existing page-level flash translation layers.

关键词： Flash translation layer Temporal locality Spatial locality Access frequency

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and computing 2018年 700卷 v-vi页

作者： Ghazali, Rozaida Deris, Mustafa Mat Nawi, Nazri Mohd Abawajy, Jemal H. Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Malaysia School of Information Technology Deakin University GeelongVIC Australia

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Proceedings - 2017 3rd International Conference on Big Data ...

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Proceedings - 2017 3rd international conference on Big data computing and Communications, BigCom 2017 2017年 xi页

作者： Qin, Zhiguang Cheng, Xiuzhen Hou, Mengshu George Washington University United States University of Electronic Science and Technology of China China

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