Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00098
Hongwei Duan, Runmeng Du, Qiong Wei, Wenli Wang, Xin Liu
Secure multiparty computation is a research focus in the international cryptographic community and a key privacy preserving technology in cyberspaces. Secure multiparty computation for the weighted average problem has great significance, and there are a wide range of applications for this problem. However, there are only a few solutions to this problem at present. In addition, to the best of our knowledge, most of the existing secure multiparty computation schemes for weighted average cannot exclude the influence of invalid data on the weighted average. To address this challenge, in this paper, we propose a corresponding solution to eliminate the influence of invalid data on the final result, and specific number of participants who give invalid datas will not be revealed. A new secure multiparty computation protocol for weighted average are designed to effectively resist different levels of collusion attacks, and the security analysis and performance analysis of the protocol is given. Finally, secure multiparty computation protocol for the weighted average problem is applied to solve secure data aggregation problem, secure weighted voting problem, and privacy-preserving location proximity detection.
{"title":"Efficient Collusion-Tolerable Secure Multiparty Computation of Weighted Average","authors":"Hongwei Duan, Runmeng Du, Qiong Wei, Wenli Wang, Xin Liu","doi":"10.1109/CIS2018.2018.00098","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00098","url":null,"abstract":"Secure multiparty computation is a research focus in the international cryptographic community and a key privacy preserving technology in cyberspaces. Secure multiparty computation for the weighted average problem has great significance, and there are a wide range of applications for this problem. However, there are only a few solutions to this problem at present. In addition, to the best of our knowledge, most of the existing secure multiparty computation schemes for weighted average cannot exclude the influence of invalid data on the weighted average. To address this challenge, in this paper, we propose a corresponding solution to eliminate the influence of invalid data on the final result, and specific number of participants who give invalid datas will not be revealed. A new secure multiparty computation protocol for weighted average are designed to effectively resist different levels of collusion attacks, and the security analysis and performance analysis of the protocol is given. Finally, secure multiparty computation protocol for the weighted average problem is applied to solve secure data aggregation problem, secure weighted voting problem, and privacy-preserving location proximity detection.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125982686","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}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00062
Li Ma, Jingjing Qu, Yan Chen, Shiwei Wei
An improved Dynamic Clonal Selection Algorithm (IDCSA) is proposed in this paper which is used in distributed network intrusion detection system (NIDS). It aims to improve the detector's ability to recognize both the known and unknown intrusions by using the strategies of establishing rules of expert knowledge, automatic evolution of gene pools, and optimization of detector generation process. The experimental results show that the proposed IDCSA can reduce FP (false positive) and improve TP (true positive), effectively improve the detection performance and adaptability of the system.
{"title":"An Improved Dynamic Clonal Selection Algorithm Using Network Intrusion Detection","authors":"Li Ma, Jingjing Qu, Yan Chen, Shiwei Wei","doi":"10.1109/CIS2018.2018.00062","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00062","url":null,"abstract":"An improved Dynamic Clonal Selection Algorithm (IDCSA) is proposed in this paper which is used in distributed network intrusion detection system (NIDS). It aims to improve the detector's ability to recognize both the known and unknown intrusions by using the strategies of establishing rules of expert knowledge, automatic evolution of gene pools, and optimization of detector generation process. The experimental results show that the proposed IDCSA can reduce FP (false positive) and improve TP (true positive), effectively improve the detection performance and adaptability of the system.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"111 3S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121808143","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}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00076
Tianxiang Feng, Hongxia Liu, Zhonglin Wan
The talent development data of two typical departments were collected from the talent training plan and the evaluation report made by a consulting company. Based on the data, the professional construction of two departments is evaluated and compared by using four-table chi-square test. And then the relationship between the professional ability, the career maturity, the ratio of basic ability improvement, the ratio of innovation ability improvement, and the ratio of professional quality improvement are explored. Finally, some useful rules between them are discovered.
{"title":"The Quality Analysis and Reform of Professional Construction—Illustrated by the Case of Dongguan Polytechnic","authors":"Tianxiang Feng, Hongxia Liu, Zhonglin Wan","doi":"10.1109/CIS2018.2018.00076","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00076","url":null,"abstract":"The talent development data of two typical departments were collected from the talent training plan and the evaluation report made by a consulting company. Based on the data, the professional construction of two departments is evaluated and compared by using four-table chi-square test. And then the relationship between the professional ability, the career maturity, the ratio of basic ability improvement, the ratio of innovation ability improvement, and the ratio of professional quality improvement are explored. Finally, some useful rules between them are discovered.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124728918","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}
It is an NP-hard problem for prediction of RNA folding structure incuding pseudoknots, we investigate the RNA pseudoknotted structure based on characteristics of the RNA folding structure, and the Basin Hopping Graph as a novel model of RNA folding landscape structures. Our paper design an 1+ε (ε>0) polynomial time approximation scheme in searching maximum number of stackings, we give the proof of the approximation scheme in RNA pseudoknotted structure. we have also presented the computing algorithm of barrier tree based on the BHG.
{"title":"Algorithm and Scheme in RNA Structure Prediction Including Pseudoknots","authors":"Zhendong Liu, Fanghan Liu, Qingxia Kong, Fanchang Hao, Hongluan Zhao","doi":"10.1109/CIS2018.2018.00050","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00050","url":null,"abstract":"It is an NP-hard problem for prediction of RNA folding structure incuding pseudoknots, we investigate the RNA pseudoknotted structure based on characteristics of the RNA folding structure, and the Basin Hopping Graph as a novel model of RNA folding landscape structures. Our paper design an 1+ε (ε>0) polynomial time approximation scheme in searching maximum number of stackings, we give the proof of the approximation scheme in RNA pseudoknotted structure. we have also presented the computing algorithm of barrier tree based on the BHG.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131627678","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}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00035
Junyu Li, Haoliang Yuan, L. L. Lai, Yiu-ming Cheung
Representation-based classifiers have shown the impressive results for pattern classification. In this paper, we propose a joint collaborative representation and discriminative projection model (JCRDP) for subspace learning. We aim to seek a linear projection matrix to effectively reveal or maintain the underlying structure of original data and well fit collaborative representation classifier simultaneously. Unlike previous representation-based subspace learning methods, in which the linear reconstruction and the generalized eigenvalue decomposition are two independent steps, our proposed JCRDP integrates these two tasks into one single optimization step to learn a more discriminative linear projection matrix. To effectively solve JCRDP, we develop an alternative strategy to deal with the optimization problem. Extensive experimental results demonstrate the effectiveness of our proposed method.
{"title":"Joint Collaborative Representation and Discriminative Projection for Pattern Classification","authors":"Junyu Li, Haoliang Yuan, L. L. Lai, Yiu-ming Cheung","doi":"10.1109/CIS2018.2018.00035","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00035","url":null,"abstract":"Representation-based classifiers have shown the impressive results for pattern classification. In this paper, we propose a joint collaborative representation and discriminative projection model (JCRDP) for subspace learning. We aim to seek a linear projection matrix to effectively reveal or maintain the underlying structure of original data and well fit collaborative representation classifier simultaneously. Unlike previous representation-based subspace learning methods, in which the linear reconstruction and the generalized eigenvalue decomposition are two independent steps, our proposed JCRDP integrates these two tasks into one single optimization step to learn a more discriminative linear projection matrix. To effectively solve JCRDP, we develop an alternative strategy to deal with the optimization problem. Extensive experimental results demonstrate the effectiveness of our proposed method.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114804854","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}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00114
Jie Zhang, Hao Chen, Kai Wang
Decentralized original and disordered management of atmospheric lidar data are seriously obstructing regional atmospheric environment research. Though the analysis of the characteristics of atmospheric lidar data in this paper, it shows that it is very necessary to create the data standard based on metadata. After the analysis of the basic functions and the main content of the atmospheric lidar metadata standard, it gives a descriptive method and atmospheric lidar metadata model to based on meteorological metadata standard.
{"title":"Study on Standardization of Detection Data of Atmospheric Microparticle Lidar Based on Metadata","authors":"Jie Zhang, Hao Chen, Kai Wang","doi":"10.1109/CIS2018.2018.00114","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00114","url":null,"abstract":"Decentralized original and disordered management of atmospheric lidar data are seriously obstructing regional atmospheric environment research. Though the analysis of the characteristics of atmospheric lidar data in this paper, it shows that it is very necessary to create the data standard based on metadata. After the analysis of the basic functions and the main content of the atmospheric lidar metadata standard, it gives a descriptive method and atmospheric lidar metadata model to based on meteorological metadata standard.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"37 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116787526","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}
Pub Date : 2018-11-01DOI: 10.1109/cis2018.2018.00110
Yunfeng Yang, Huihui Bai, Yinchun Zheng
A portfolio optimization problem with consumption is considered.Risky asset price obeys jump-diffusion process, and the dividend is paid continuously.The goal is to choose optimal investment and consumption policies for the problem of maximizing expected total utility form both consumption and terminal wealth. It is fined unique equivalent martingale measure, we employ the conventional stochastic analysis methods. We find explicit formula for the optimal portfolio and the consumption process.In addition, for a special case where the utility is logarithmic or is a power function, an explicit formula is given.
{"title":"Optimal Consumption and Investment Strategies in a Jump-Diffusion Model","authors":"Yunfeng Yang, Huihui Bai, Yinchun Zheng","doi":"10.1109/cis2018.2018.00110","DOIUrl":"https://doi.org/10.1109/cis2018.2018.00110","url":null,"abstract":"A portfolio optimization problem with consumption is considered.Risky asset price obeys jump-diffusion process, and the dividend is paid continuously.The goal is to choose optimal investment and consumption policies for the problem of maximizing expected total utility form both consumption and terminal wealth. It is fined unique equivalent martingale measure, we employ the conventional stochastic analysis methods. We find explicit formula for the optimal portfolio and the consumption process.In addition, for a special case where the utility is logarithmic or is a power function, an explicit formula is given.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117134000","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}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00056
Tianwen Huang, Lejun Zhang, Xiaoyan Hu, Xiaoying Lei
Information hiding technology based on IP covert channel is a research hotspot. Although the existing packet ordering IP covert channel can realize data transmission, it is unable to judge the correctness of data transmission due to the lack of data verification. In this paper, a data verification method based on packet ordering IP covert channel is proposed. Two different covert data transmission methods are used for data transmission, and data verification is carried out after the transmission of data is completed. Experiments show that the method proposed in this paper can realize data verification and can use retransmission mechanism to retransmit data after data is different.
{"title":"A Data Validation Method Based on IP Covert Channel Packet Ordering","authors":"Tianwen Huang, Lejun Zhang, Xiaoyan Hu, Xiaoying Lei","doi":"10.1109/CIS2018.2018.00056","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00056","url":null,"abstract":"Information hiding technology based on IP covert channel is a research hotspot. Although the existing packet ordering IP covert channel can realize data transmission, it is unable to judge the correctness of data transmission due to the lack of data verification. In this paper, a data verification method based on packet ordering IP covert channel is proposed. Two different covert data transmission methods are used for data transmission, and data verification is carried out after the transmission of data is completed. Experiments show that the method proposed in this paper can realize data verification and can use retransmission mechanism to retransmit data after data is different.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123233508","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}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00044
X. Guo, Ming Zhang, Yongqiang Dai
Image segmentation is a key step in feature extraction and disease recognition of plant diseases images. To avoid the subjectivity of using traditional PCNN (pulse-coupled neural network) to segment plant disease image, a new image segmentation model (SFLA-PCNN) is proposed in this paper to get the parameters configuration of PCNN. The weighted sum of cross entropy and compactness degree of image segmentation is chosen as fitness function of shuffled frog leap algorithm to optimize the parameters PCNN, which could improve the performance of PCNN. After 100 times local iteration and 1500 times global iteration, we get the best parameter configure. The extensive tests prove that SFLA-PCNN model could be used to extract the lesion from the background effectively, which could provide a foundation for following disease diagnose.
{"title":"Image of Plant Disease Segmentation Model Based on Pulse Coupled Neural Network with Shuffle Frog Leap Algorithm","authors":"X. Guo, Ming Zhang, Yongqiang Dai","doi":"10.1109/CIS2018.2018.00044","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00044","url":null,"abstract":"Image segmentation is a key step in feature extraction and disease recognition of plant diseases images. To avoid the subjectivity of using traditional PCNN (pulse-coupled neural network) to segment plant disease image, a new image segmentation model (SFLA-PCNN) is proposed in this paper to get the parameters configuration of PCNN. The weighted sum of cross entropy and compactness degree of image segmentation is chosen as fitness function of shuffled frog leap algorithm to optimize the parameters PCNN, which could improve the performance of PCNN. After 100 times local iteration and 1500 times global iteration, we get the best parameter configure. The extensive tests prove that SFLA-PCNN model could be used to extract the lesion from the background effectively, which could provide a foundation for following disease diagnose.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130304191","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}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00018
M. Jiang, Z. Meng, Gengui Zhou, R. Shen
In this paper, a smoothing penalty function for two-cardinality sparse constrained optimization problems is presented. The paper proves that this type of the smoothing penalty functions has good properties in helping to solve two-cardinality sparse constrained optimization problems. Moreover, based on the penalty function, an algorithm is presented to solve the two-cardinality sparse constrained optimization problems, with its convergence under some conditions proved. A numerical experiment shows that a satisfactory approximate optimal solution can be obtained by the proposed algorithm.
{"title":"A Smoothing Penalty Function Algorithm for Two-Cardinality Sparse Constrained Optimization Problems","authors":"M. Jiang, Z. Meng, Gengui Zhou, R. Shen","doi":"10.1109/CIS2018.2018.00018","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00018","url":null,"abstract":"In this paper, a smoothing penalty function for two-cardinality sparse constrained optimization problems is presented. The paper proves that this type of the smoothing penalty functions has good properties in helping to solve two-cardinality sparse constrained optimization problems. Moreover, based on the penalty function, an algorithm is presented to solve the two-cardinality sparse constrained optimization problems, with its convergence under some conditions proved. A numerical experiment shows that a satisfactory approximate optimal solution can be obtained by the proposed algorithm.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122396201","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}
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