2017 13th International Conference on Computational Intelligence and Security (CIS)最新文献

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Pedestrian Detection Method Based on Faster R-CNN 基于更快R-CNN的行人检测方法
Hui Zhang, Yu Du, Shu Ning, Yonghua Zhang, Shuo Yang, Chengpeng Du
{"title":"Pedestrian Detection Method Based on Faster R-CNN","authors":"Hui Zhang, Yu Du, Shu Ning, Yonghua Zhang, Shuo Yang, Chengpeng Du","doi":"10.1109/CIS.2017.00099","DOIUrl":"https://doi.org/10.1109/CIS.2017.00099","url":null,"abstract":"Pedestrian detection based on computer vision is an important branch of object recognition, which is applied to intelligent monitoring, intelligent driving, robot and so on. At present, many pedestrian detection methods are proposed. However, because of the complexity of the background, pedestrian posture diversity and pedestrian occlusions, pedestrian detection is still a challenge which calls for precise algorithms. In this paper, the fast Region-based Convolutional Neural Network (Faster R-CNN) is used. Firstly, image features were extracted by CNN. After that, we built up a Region Proposal Network to extract regions that might contain pedestrians combined with K-means cluster analysis. And the region is identified and classified by detection network. Finally, the method was tested in the INRIA data set. The results show that the method of pedestrian detection based on Faster R-CNN, which achieves the accuracy of 92.7%, performs better, compared with other algorithms.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114505665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 34
Sensors Validation Based on Bayesian Classifiers 基于贝叶斯分类器的传感器验证
Peng Sun, Zi-yan Wu, Haifeng Yang, Xiaoxiao Liu, Kang Chen
{"title":"Sensors Validation Based on Bayesian Classifiers","authors":"Peng Sun, Zi-yan Wu, Haifeng Yang, Xiaoxiao Liu, Kang Chen","doi":"10.1109/CIS.2017.00028","DOIUrl":"https://doi.org/10.1109/CIS.2017.00028","url":null,"abstract":"Validation of Sensors has very important effects on the consequences of structural experiments and subsequent analyzing works. This article focus on the problem that if the data collected from the sensors are valid or not. It tested the validation of an target acceleration sensor on a truss structure by using Naive Bayesian Classifier and Tree Augmented Naive Bayesian Classifier which are based on machine learning technology whose theory basis is probability statistics. In the course of data analyzing, the theoretical values modified by Finite Element Modeling are taken as an criterion of data collected from sensors. The continuous data are discretized by several different discretization methods. Both of the classifiers are created by discretized training data and used to test the validation of the specified sensor. The comparison between two experiments based on NBC and TAN is presented. It is proved that both the testing methods are effective.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121906339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Reliability Prediction for Web Service Composition Web服务组合的可靠性预测
Weitao Ha
{"title":"Reliability Prediction for Web Service Composition","authors":"Weitao Ha","doi":"10.1109/CIS.2017.00132","DOIUrl":"https://doi.org/10.1109/CIS.2017.00132","url":null,"abstract":"The key issues in the development of Web service composition are the dynamic and efficient reliability prediction. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. This paper proposes a reliability prediction model based on Petri net. For atomic services, a staged reliability model is provided which predicts reliability from network environment availability, hermit equipment availability, discovery reliability and binding reliability. To address the complex connecting relationship among subservices, places of basic Petri net for Input and Output are extended to some subtypes for multi-source input place and multiuse output place. The approach has been implemented and has been used in the context of travel process mining. Although the results are presented in the context of Petri nets, the approach can be applied to any process modeling language with executable semantics.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123982571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
A Survey of Network Reconstruction on Social Network 基于社会网络的网络重构研究综述
Longfei Wang, Yong Zeng, Zhongyuan Jiang, Zhihong Liu, Jianfeng Ma
{"title":"A Survey of Network Reconstruction on Social Network","authors":"Longfei Wang, Yong Zeng, Zhongyuan Jiang, Zhihong Liu, Jianfeng Ma","doi":"10.1109/CIS.2017.00071","DOIUrl":"https://doi.org/10.1109/CIS.2017.00071","url":null,"abstract":"Social network contains more and more sensitive information, which makes the analysis of it more valuable. Network reconstruction is an important method for network analysis which is worthy to further study. In order to have a deep comprehension of the methods and the classification of network reconstruction, we need to do a classification and analysis for it. In this paper, network reconstruction on social network is the main research direction. Firstly, we introduce some concepts of privacy, protection methods and attack methods for social networks. After that we lead to the concept and usage of network reconstruction. Secondly, the existing methods of network reconstruction are introduced and classified based on the sources of observation data: information from node and information from network structure. And then we classify its application. Finally, we analyze the problem and challenge of existing methods. Meanwhile, some research points are also summarized to indicate the direction for other researchers and future work.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125030095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Challenges for the Comprehensive and Integrated Information Security Management 全面集成信息安全管理面临的挑战
J. Anttila, Kari Jussila
{"title":"Challenges for the Comprehensive and Integrated Information Security Management","authors":"J. Anttila, Kari Jussila","doi":"10.1109/CIS.2017.00136","DOIUrl":"https://doi.org/10.1109/CIS.2017.00136","url":null,"abstract":"Information security management needs to be considered from the perspective of individuals, organizations and the society as a whole. The current situation is not satisfactory with regard to the concepts or practices and is becoming more challenging in the future. Further research and development of the managerial methodologies and practices are necessary for the needs of the new business environments, SMEs and startups. This our research focuses on the comprehensive and multi-disciplinary framework that aims at providing challenges for the new assorted research initiatives and innovations, and insight and guidance for the implementers who integrate the information security solutions within the management of business systems and processes together with other specialized managerial viewpoints. At present, the studies and practical implementations are very scattered and separate from each other, and difficult to be reconciled. Also effective collaboration of the administrative authorities, business leaders and security specialists, and effective links between the managerial, human and technical viewpoints are emphasized.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128382623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A KNN Match Based Tracking-Learning-Detection Method with Adjustment of Surveyed Areas 基于KNN匹配的测量区域平差跟踪-学习-检测方法
Yumin Tian, Lin Deng, Qiang Li
{"title":"A KNN Match Based Tracking-Learning-Detection Method with Adjustment of Surveyed Areas","authors":"Yumin Tian, Lin Deng, Qiang Li","doi":"10.1109/CIS.2017.00104","DOIUrl":"https://doi.org/10.1109/CIS.2017.00104","url":null,"abstract":"A Tracking-Learning-Detection (TLD) is a tracking framework that decomposes the long-term tracking task into tracking, learning and detection. To improve the tracking accuracy and precision with TLD, the constraint that a target cannot move a significant distance within a short time period with limited velocity is added, and the target displacement in the previous frame analyzed is used to reduce the detection range of the TLD detector. Instead of using the Median Flow Tracker, a K-Nearest Neighbor (Knn) matching with the Fused Lukas-Kanade method with ORB feature points is used to track targets. Experiments demonstrate that the improved TLD algorithm has better tracking accuracy.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130227096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Enhancing Anonymity of Bitcoin Based on Ring Signature Algorithm 基于环签名算法增强比特币匿名性
Yi Liu, Ruilin Li, Xingtong Liu, Jian Wang, Chaojing Tang, Hongyan Kang
{"title":"Enhancing Anonymity of Bitcoin Based on Ring Signature Algorithm","authors":"Yi Liu, Ruilin Li, Xingtong Liu, Jian Wang, Chaojing Tang, Hongyan Kang","doi":"10.1109/CIS.2017.00075","DOIUrl":"https://doi.org/10.1109/CIS.2017.00075","url":null,"abstract":"Bitcoin is a decentralized digital currency, widely used for its perceived anonymity property, and has surged in popularity in recent years. Bitcoin publishes the complete transaction history in a public ledger, under pseudonyms of users. This is an alternative way to prevent double-spending attack instead of central authority. Therefore, if pseudonyms of users are attached to their identities in real world, the anonymity of Bitcoin will be a serious vulnerability. It is necessary to enhance anonymity of Bitcoin by a coin mixing service or other modifications in Bitcoin protocol. But in a coin mixing service, the relationship among input and output addresses is not hidden from the mixing service provider. So the mixing server still has the ability to track the transaction records of Bitcoin users. To solve this problem, We present a new coin mixing scheme to ensure that the relationship between input and output addresses of any users is invisible for the mixing server. We make use of a ring signature algorithm to ensure that the mixing server can't distinguish specific transaction from all these addresses. The ring signature ensures that a signature is signed by one of its users in the ring and doesn't leak any information about who signed it. Furthermore, the scheme is fully compatible with existing Bitcoin protocol and easily to scale for large amount of users.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"1587 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129172262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
LSSS Matrix-Based Attribute-Based Encryption on Lattices 基于矩阵的基于格属性的LSSS加密
Jian Zhao, Haiying Gao
{"title":"LSSS Matrix-Based Attribute-Based Encryption on Lattices","authors":"Jian Zhao, Haiying Gao","doi":"10.1109/CIS.2017.00062","DOIUrl":"https://doi.org/10.1109/CIS.2017.00062","url":null,"abstract":"Attribute-Based Encryption (ABE) schemes show unprecedented flexibility and expressiveness through the introduction of access policies. Compared to ABE schemes for thresholds or circuits from lattices, Linear Secret Sharing Schemes (LSSS) matrix-based ABE is more difficult to design for its abstract mathematical structure. We propose an ABE scheme for LSSS matrix from lattices in this work. The prior lattice-based ABE scheme for LSSS matrix constructed a large virtual encryption matrix to embed the LSSS matrix in secret key. We use a completely different but common method in lattice-based encryption schemes to achieve the same task. Moreover, we prove that our scheme is secure against chosen plaintext attack in the selective security model under the Learning with Errors (LWE) assumptions.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126909493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
A Novel Multi-objective Evolutionary Algorithm Based on a Further Decomposition Strategy 一种基于进一步分解策略的多目标进化算法
Songbai Liu, Qiuzhen Lin, Jianyong Chen
{"title":"A Novel Multi-objective Evolutionary Algorithm Based on a Further Decomposition Strategy","authors":"Songbai Liu, Qiuzhen Lin, Jianyong Chen","doi":"10.1109/CIS.2017.00014","DOIUrl":"https://doi.org/10.1109/CIS.2017.00014","url":null,"abstract":"In multi-objective evolutionary algorithms (MOEAs) based on the constrained decomposition approach, the closest sub objective space to the sub-problem is treated as a feasible region for this sub-problem, where the solutions are regarded to be better than that outside it. This approach is expected to maintain the population's diversity. However, due to the inconsistency of the weight vectors and the current population, it leads to the disequilibrium of sub-problems that a lot of individuals may be located around one sub-problem, which obviously hampers the population's diversity. Thus, this paper suggests a novel MOEA based on a further decomposition strategy (MOEA/FD). The parents and offspring populations all with the size N are combined to a union population with 2N solutions and then they are associated to the preset N weight vectors using the constrained decomposition approach. Then, the number of sub-problems with no associated solution can be computed, and the sub-problem associated with the largest number of solutions is iteratively found to further decompose it into two sub-problems, which is achieved by using a clustering method. At last, N decomposed sub-problems can be found with no less than one solution in their feasible regions. At last, in each feasible region, a simple convergence indicator is used to select a well converged solution for next evolution. When compared to six competitive MOEAs, MOEA/FD presents some advantages on tackling seventeen well-known test problems.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121722464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Bayesian Spatial Nonparametric Models for Confounding Manifest Variables with an Application to China Earthquake Data 混杂显变量贝叶斯空间非参数模型在中国地震资料中的应用
Yingzi Fu, Dexin Ren
{"title":"Bayesian Spatial Nonparametric Models for Confounding Manifest Variables with an Application to China Earthquake Data","authors":"Yingzi Fu, Dexin Ren","doi":"10.1109/CIS.2017.00049","DOIUrl":"https://doi.org/10.1109/CIS.2017.00049","url":null,"abstract":"We consider a Bayesian nonparametric models for spatial data of mixed category. Moreover, we adopt joint modeling strategy by assuming that responses and confounding variables are corresponding to continuous latent variables with multivariate Gaussian distribution. The model is built on a class of Gaussian Conditional Autoregressive (CAR) models, in combination with dependent sampling models (SSM) as well as probit stick-breaking process prior for accounting for complex interactions and high correlations of data. The key idea is to introducing spatial dependence by modeling the weights via probit transformation of Gaussian Markov random fields or discrete random probability measures of SSM. We illustrate the usefulness and effectiveness of the methodology through a real example from a China earthquake data set.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125314863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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